U.S. patent number 10,930,367 [Application Number 14/738,483] was granted by the patent office on 2021-02-23 for methods, models, systems, and apparatus for identifying target sequences for cas enzymes or crispr-cas systems for target sequences and conveying results thereof.
This patent grant is currently assigned to THE BROAD INSTITUTE, INC., MASSACHUSETTS INSTITUTE OF TECHNOLOGY, PRESIDENT AND FELLOWS OF HARVARD COLLEGE. The grantee listed for this patent is THE BROAD INSTITUTE INC., MASSACHUSETTS INSTITUTE OF TECHNOLOGY, PRESIDENT AND FELLOWS OF HARVARD COLLEGE. Invention is credited to Patrick Hsu, Yinqing Li, David Arthur Scott, Joshua Asher Weinstein, Feng Zhang.
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United States Patent |
10,930,367 |
Zhang , et al. |
February 23, 2021 |
Methods, models, systems, and apparatus for identifying target
sequences for Cas enzymes or CRISPR-Cas systems for target
sequences and conveying results thereof
Abstract
Disclosed are thermodynamic and multiplication methods
concerning CRISPR-Cas systems, and apparatus therefor.
Inventors: |
Zhang; Feng (Cambridge, MA),
Li; Yinqing (Cambridge, MA), Scott; David Arthur
(Cambridge, MA), Weinstein; Joshua Asher (Cambridge, MA),
Hsu; Patrick (Cambridge, MA) |
Applicant: |
Name |
City |
State |
Country |
Type |
THE BROAD INSTITUTE INC.
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
PRESIDENT AND FELLOWS OF HARVARD COLLEGE |
Cambridge
Cambridge
Cambridge |
MA
MA
MA |
US
US
US |
|
|
Assignee: |
THE BROAD INSTITUTE, INC.
(Cambridge, MA)
MASSACHUSETTS INSTITUTE OF TECHNOLOGY (Cambridge, MA)
PRESIDENT AND FELLOWS OF HARVARD COLLEGE (Cambridge,
MA)
|
Family
ID: |
1000005379109 |
Appl.
No.: |
14/738,483 |
Filed: |
June 12, 2015 |
Prior Publication Data
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Document
Identifier |
Publication Date |
|
US 20150356239 A1 |
Dec 10, 2015 |
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Related U.S. Patent Documents
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Application
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Filing Date |
Patent Number |
Issue Date |
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PCT/US2013/074812 |
Dec 12, 2013 |
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61836080 |
Jun 17, 2013 |
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61758468 |
Jan 30, 2013 |
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61769046 |
Feb 25, 2013 |
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61802174 |
Mar 15, 2013 |
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61806375 |
Mar 28, 2013 |
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61814263 |
Apr 20, 2013 |
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61819803 |
May 6, 2013 |
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61828130 |
May 28, 2013 |
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61736527 |
Dec 12, 2012 |
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61748427 |
Jan 2, 2013 |
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61791409 |
Mar 15, 2013 |
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61835931 |
Jun 17, 2013 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16B
20/00 (20190201); C12N 15/113 (20130101); C12N
9/22 (20130101); C12N 15/1089 (20130101); C12N
2320/11 (20130101); C12N 2310/20 (20170501); C12N
15/1082 (20130101) |
Current International
Class: |
G16B
20/00 (20190101); C12N 9/22 (20060101); C12N
15/113 (20100101); C12N 15/10 (20060101) |
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|
Primary Examiner: Woitach; Joseph
Attorney, Agent or Firm: Foley & Lardner LLP
Government Interests
STATEMENT AS TO FEDERALLY SPONSORED RESEARCH
This invention was made with government support under Grant Nos.
MH100706 and DK097768 awarded by the National Institutes of Health.
The government has certain rights in the invention.
Parent Case Text
RELATED APPLICATIONS AND INCORPORATION BY REFERENCE
This application is a continuation-in-part of international patent
application serial no. PCT/US2013/074812 filed Dec. 12, 2013 and
published as WO2014093709, which claims priority to U.S.
provisional patent application 61/836,080 entitled METHODS,
SYSTEMS, AND APPARATUS FOR IDENTIFYING TARGET SEQUENCES FOR CAS
ENZYMES OR CRISPR-CAS SYSTEMS FOR TARGET SEQUENCES AND CONVEYING
RESULTS THEREOF filed on Jun. 17, 2013, and which also claims
priority to US provisional patent applications 61/758,468;
61/769,046; 61/802,174; 61/806,375; 61/814,263; 61/819,803 and
61/828,130 each entitled ENGINEERING AND OPTIMIZATION OF SYSTEMS,
METHODS AND COMPOSITIONS FOR SEQUENCE MANIPULATION, filed on Jan.
30, 2013; Feb. 25, 2013; Mar. 15, 2013; Mar. 28, 2013; Apr. 20,
2013; May 6, 2013 and May 28, 2013, respectively, and which also
claims priority to US provisional patent applications 61/736,527
and 61/748,427, both entitled SYSTEMS METHODS AND COMPOSITIONS FOR
SEQUENCE MANIPULATION filed on Dec. 12, 2012 and Jan. 2, 2013,
respectively, and which also claims priority to US provisional
patent applications 61/791,409 and 61/835,931 filed on Mar. 15,
2013 and Jun. 17, 2013 respectively.
Claims
What is claimed is:
1. A method for selecting and producing an engineered CRISPR
complex for targeting and/or cleavage of a candidate target nucleic
acid sequence within a eukaryotic cell, comprising the steps of:
(a) determining amount, location and nature of mismatch(es) of
guide sequence of potential CRISPR complex(es) and the candidate
target nucleic acid sequence, (b) determining contribution of each
of the amount, location and nature of mismatch(es) to hybridization
free energy of binding between the target nucleic acid sequence and
the guide sequence of potential CRISPR complex(es) from a training
data set, (c) based on the contribution analysis of step (b),
predicting cleavage at the location(s) of the mismatch(es) of the
target nucleic acid sequence by the potential CRISPR complex(es),
(d) selecting the CRISPR complex from potential CRISPR complex(es)
based on whether the prediction of step (c) indicates that it is
more likely than not that cleavage will occur at location(s) of
mismatch(es) by the CRISPR complex; (e) producing the selected
CRISPR complex or nucleic acid molecule(s) encoding the selected
CRISPR complex for targeting and/or cleavage of the candidate
target nucleic acid sequence within the eukaryotic cell; and (f)
delivering the selected CRISPR complex or nucleic acid molecule(s)
encoding the selected CRISPR complex into the eukaryotic cell,
wherein the selected CRISPR complex targets and/or cleaves the
candidate target nucleic acid sequence within the eukaryotic
cell.
2. The method of claim 1 wherein the candidate target sequence is a
DNA sequence, and the mismatch(es) are of RNA of potential CRISPR
complex(es) and the DNA.
3. The method of claim 1, wherein step (b) is performed by
determining known local free energies, .DELTA.Gij(k), between every
guide RNA sequence i and target DNA nucleic acid sequence j at
position k, calculating values of the effective free-energy
Z.sub.ij using the relationship
p.sub.ij.varies.e.sup.-.beta.Zij,where p.sub.ij is measured cutting
frequency by guide RNA sequence i on target DNA nucleic acid
sequence j in the training set and .beta. is a positive constant of
proportionality, determining the weights which are
position-dependent weights .alpha..sub.k by fitting the known value
of .DELTA.Gij(k) and the calculated value of Z.sub.ij across each
guide RNA/target DNA sequence pair in the training set in the sum
across all N bases of the guide-sequence
.times..alpha..times..DELTA..times..function. ##EQU00008## by
writing the above equation in the matrix form {right arrow over
(Z)}=G{right arrow over (.alpha.)} and wherein, step (c) is
performed by estimating the effective free-energy Zest using the
determined position dependent weights in the equation {right arrow
over (Z.sub.est)}=G {right arrow over (.alpha.)} and determining
estimated spacer-target cutting frequencies p.sub.est
.alpha.e.sup.-.beta.Zest, to thereby predict cleavage.
4. The method of claim 2 wherein the distance, in bp, between the
first and last base of the target sequence is 18.
5. The method of claim 1 wherein predicting cleavage comprises
predicting whether cleavage is more likely than not to occur at
location(s) of mismatch(es), and thereby predicting cleavage.
6. The method of claim 1, further comprising normalizing the
calculated values of the effective free energy of hybridization Z
for each guide RNA/target DNA sequence pair in the training
set.
7. The method of claim 1, further comprising filtering out
calculated value of the effective free energy of hybridization Z
for each guide RNA/target DNA sequence pair in the training set
which have a sequencing depth which is below a minimum sequencing
depth.
8. The method of claim 1, wherein the method is implemented by a
computer system comprising: a. a memory unit configured to receive
and/or store sequence information of the candidate target nucleic
acid sequence; and b. one or more processors alone or in
combination programmed to perform steps (a) to (d).
9. The method of claim 1, wherein step (b) is performed by:
defining a thermodynamic model having a set of weights linking
effective free energy of hybridization Z to local free energies G;
defining a training set of the guide sequence/target DNA sequence
pairs; inputting known values of local free energies G for each
guide sequence/target DNA sequence pair in the training set;
calculating a value of effective free energy of hybridization Z for
each guide sequence/target DNA sequence pair in the training set;
determining the weights using a machine learning algorithm, and
outputting the weights whereby the weights can be used to estimate
the free energy of hybridization for any sequence.
10. The method of claim 1 wherein the guide sequence is comprised
within a single guide RNA (sgRNA) or within a CRISPR-Cas system
chimera RNA (chiRNA).
11. The method of claim 1, wherein the selected CRISPR complex
generates a cleavage within the candidate target nucleic acid
sequence within the eukaryotic cell.
Description
Reference is also made to international patent applications
PCT/US2013/074611 filed Dec. 12, 2013 and published as WO
2014/093595; PCT/US2013/074743 filed Dec. 12, 2013 and published as
WO 2014/093661; PCT/US2013/074790 filed Dec. 12, 2013 and published
as WO 2014/093694; PCT/US2013/074825 filed Dec. 12, 2013 and
published as WO 2014/093718; PCT/US2013/074667 filed Dec. 12, 2013
and published as WO 2014/093622; PCT/US2013/074691 filed Dec. 12,
2013 and published as WO 2014/093635; PCT/US2013/074736 filed Dec.
12, 2013 and published as WO 2014/093655; PCT/US2013/074819 filed
Dec. 12, 2013 and published as WO 2014/093712; and
PCT/US2013/074800 filed Dec. 12, 2013 and published as WO
2014/093701.
Reference is also made to US provisional patent applications
61/757,972, filed Jan. 29, 2013, 61/799,800 filed Mar. 15, 2013,
61/835,936, 61/835,973, 61/836,080, 61/836,101, 61/836,123,
61/836,127, and 61/847,537, each filed Jun. 17, 2013, 61/862,468
and 61/862,355, each filed Aug. 5, 2013; 61/871,301 filed Aug. 28,
2013; 61/969,777 filed Sep. 25, 2013; and 61/961,980 filed Oct. 28,
2013.
The foregoing applications, and all documents cited therein or
during their prosecution ("appln cited documents") and all
documents cited or referenced in the appln cited documents, and all
documents cited or referenced herein ("herein cited documents"),
and all documents cited or referenced in herein cited documents,
together with any manufacturer's instructions, descriptions,
product specifications, and product sheets for any products
mentioned herein or in any document incorporated by reference
herein, are hereby incorporated herein by reference, and may be
employed in the practice of the invention. More specifically, all
referenced documents are incorporated by reference to the same
extent as if each individual document was specifically and
individually indicated to be incorporated by reference.
FIELD OF THE INVENTION
The present invention generally relates to the engineering and
optimization of systems, methods and compositions used for the
control of gene expression involving sequence targeting, such as
genome perturbation or gene-editing, that relate to Clustered
Regularly Interspaced Short Palindromic Repeats (CRISPR) and
components thereof.
BACKGROUND OF THE INVENTION
The CRISPR/Cas or the CRISPR-Cas system (both terms are used
interchangeably throughout this application) does not require the
generation of customized proteins to target specific sequences but
rather a single Cas enzyme can be programmed by a short RNA
molecule to recognize a specific DNA target. Adding the CRISPR-Cas
system to the repertoire of genome sequencing techniques and
analysis methods may significantly simplify the methodology and
accelerate the ability to catalog and map genetic factors
associated with a diverse range of biological functions and
diseases. To utilize the CRISPR-Cas system effectively for genome
editing without deleterious effects, it is critical to understand
methods, systems and apparatus for identifying target sequences for
Cas enzymes or CRISPR-Cas systems for target sequences of interest
and conveying the results, which are aspects of the claimed
invention.
SUMMARY OF THE INVENTION
The CRISPR/Cas or the CRISPR-Cas system (both terms may be used
interchangeably throughout this application) does not require the
generation of customized proteins to target specific sequences but
rather a single Cas enzyme can be programmed by a short RNA
molecule to recognize a specific DNA target, in other words the Cas
enzyme can be recruited to a specific DNA target using said short
RNA molecule. Adding the CRISPR-Cas system to the repertoire of
genome sequencing techniques and analysis methods may significantly
simplify the methodology and accelerate the ability to catalog and
map genetic factors associated with a diverse range of biological
functions and diseases. To utilize the CRISPR-Cas system
effectively for genome editing without deleterious effects, it is
critical to understand aspects of engineering and optimization of
these genome engineering tools, which are aspects of the claimed
invention.
In some aspects the invention relates to a non-naturally occurring
or engineered composition comprising a CRISPR/Cas system chimeric
RNA (chiRNA) polynucleotide sequence, wherein the polynucleotide
sequence comprises (a) a guide sequence capable of hybridizing to a
target sequence in a eukaryotic cell, (b) a tracr mate sequence,
and (c) a tracr sequence wherein (a), (b) and (c) are arranged in a
5' to 3' orientation, wherein when transcribed, the tracr mate
sequence hybridizes to the tracr sequence and the guide sequence
directs sequence-specific binding of a CRISPR complex to the target
sequence, wherein the CRISPR complex comprises a CRISPR enzyme
complexed with (1) the guide sequence that is hybridized to the
target sequence, and (2) the tracr mate sequence that is hybridized
to the tracr sequence,
or
an CRISPR enzyme system, wherein the system is encoded by a vector
system comprising one or more vectors comprising I. a first
regulatory element operably linked to a CRISPR/Cas system chimeric
RNA (chiRNA) polynucleotide sequence, wherein the polynucleotide
sequence comprises (a) one or more guide sequences capable of
hybridizing to one or more target sequences in a eukaryotic cell,
(b) a tracr mate sequence, and (c) one or more tracr sequences, and
II. a second regulatory element operably linked to an enzyme-coding
sequence encoding a CRISPR enzyme comprising at least one or more
nuclear localization sequences, wherein (a), (b) and (c) are
arranged in a 5' to 3'orientation, wherein components I and II are
located on the same or different vectors of the system, wherein
when transcribed, the tracr mate sequence hybridizes to the tracr
sequence and the guide sequence directs sequence-specific binding
of a CRISPR complex to the target sequence, wherein the CRISPR
complex comprises the CRISPR enzyme complexed with (1) the guide
sequence that is hybridized to the target sequence, and (2) the
tracr mate sequence that is hybridized to the tracr sequence,
or
a multiplexed CRISPR enzyme system, wherein the system is encoded
by a vector system comprising one or more vectors comprising I. a
first regulatory element operably linked to (a) one or more guide
sequences capable of hybridizing to a target sequence in a cell,
and (b) at least one or more tracr mate sequences, II. a second
regulatory element operably linked to an enzyme-coding sequence
encoding a CRISPR enzyme, and III. a third regulatory element
operably linked to a tracr sequence, wherein components I, II and
III are located on the same or different vectors of the system,
wherein when transcribed, the tracr mate sequence hybridizes to the
tracr sequence and the guide sequence directs sequence-specific
binding of a CRISPR complex to the target sequence, wherein the
CRISPR complex comprises the CRISPR enzyme complexed with (1) the
guide sequence that is hybridized to the target sequence, and (2)
the tracr mate sequence that is hybridized to the tracr sequence,
and wherein in the multiplexed system multiple guide sequences and
a single tracr sequence is used.
Without wishing to be bound by theory, it is believed that the
target sequence should be associated with a PAM (protospacer
adjacent motif); that is, a short sequence recognized by the CRISPR
complex. This PAM may be considered a CRISPR motif.
With regard to the CRISPR system or complex discussed herein,
reference is made to FIG. 2. FIG. 2 shows an exemplary CRISPR
system and a possible mechanism of action (A), an example
adaptation for expression in eukaryotic cells, and results of tests
assessing nuclear localization and CRISPR activity (B-F).
The invention provides a method of identifying one or more unique
target sequences. The target sequences may be in a genome of an
organism, such as a genome of a eukaryotic organism. Accordingly,
through potential sequence-specific binding, the target sequence
may be susceptible to being recognized by a CRISPR-Cas system.
(Likewise, the invention thus comprehends identifying one or more
CRISPR-Cas systems that identifies one or more unique target
sequences.) The target sequence may include the CRISPR motif and
the sequence upstream or before it. The method may comprise:
locating a CRISPR motif, e.g., analyzing (for instance comparing) a
sequence to ascertain whether a CRISPR motif. e.g., a PAM sequence,
a short sequence recognized by the CRISPR complex, is present in
the sequence; analyzing (for instance comparing) the sequence
upstream of the CRISPR motif to determine if that upstream sequence
occurs elsewhere in the genome; selecting the upstream sequence if
it does not occur elsewhere in the genome, thereby identifying a
unique target site. The sequence upstream of the CRISPR motif may
be at least 10 bp or at least 11 bp or at least 12 bp or at least
13 bp or at least 14 bp or at least 15 bp or at least 16 bp or at
least 17 bp or at least 18 bp or at least 19 bp or at least 20 bp
in length, e.g., the sequence upstream of the CRISPR motif may be
about 10 bp to about 20 bp, e.g., the sequence upstream is 10, 11,
12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
29 or 30 bp in length. The CRISPR motif may be recognized by a Cas
enzyme such as a Cas9 enzyme, e.g., a SpCas9 enzyme. Further, the
CRISPR motif may be a protospacer-adjacent motif (PAM) sequence,
e.g., NGG or NAG. Accordingly, as CRISPR motifs or PAM sequences
may be recognized by a Cas enzyme in vitro, ex vivo or in vivo, in
the in silico analysis, there is an analysis, e.g., comparison, of
the sequence in interest against CRISPR motifs or PAM sequences to
identify regions of the sequence in interest which may be
recognized by a Cas enzyme in vitro, ex vivo or in vivo. When that
analysis identifies a CRISPR motif or PAM sequence, the next
analysis e.g., comparison is of the sequences upstream from the
CRISPR motif or PAM sequence, e.g., analysis of the sequence 10,
11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,
28, 29 or 30 bp in length starting at the PAM or CRISPR motif and
extending upstream therefrom. That analysis is to see if that
upstream sequence is unique, i.e., if the upstream sequence does
not appear to otherwise occur in a genome, it may be a unique
target site. The selection for unique sites is the same as the
filtering step: in both cases, you filter away all target sequences
with associated CRISPR motif that occur more than once in the
target genome.
Eukaryotic organisms of interest may include but are not limited to
Homo sapiens (human), Mus musculus (mouse), Rattus norvegicus
(rat), Danio rerio (zebrafish), Drosophila melanogaster (fruit
fly), Caenorhabditis elegans (roundworm), Sus scrofa (pig) and Bos
taurus (cow). The eukaryotic organism can be selected from the
group consisting of Homo sapiens (human), Mus musculus (mouse),
Rattus norvegicus (rat), Danio rerio (zebrafish), Drosophila
melanogaster (fruit fly), Caenorhabditis elegans (roundworm), Sus
scrofa (pig) and Bos taurus (cow). The invention also comprehends
computer-readable medium comprising codes that, upon execution by
one or more processors, implements a herein method of identifying
one or more unique target sequences.
The invention further comprehends a computer system for identifying
one or more unique target sequences, e.g., in a genome, such as a
genome of a eukaryotic organism, the system comprising: a. a memory
unit configured to receive and/or store sequence information of the
genome; and b. one or more processors alone or in combination
programmed to perform a herein method of identifying one or more
unique target sequences (e.g., locate a CRISPR motif, analyze a
sequence upstream of the CRISPR motif to determine if the sequence
occurs elsewhere in the genome, select the sequence if it does not
occur elsewhere in the genome), to thereby identifying a unique
target site and display and/or transmit the one or more unique
target sequences. The candidate target sequence may be a DNA
sequence. Mismatch(es) can be of RNA of the CRISPR complex and the
DNA. In aspects of the invention, susceptibility of a target
sequence being recognized by a CRISPR-Cas system indicates that
there may be stable binding between the one or more base pairs of
the target sequence and guide sequence of the CRISPR-Cas system to
allow for specific recognition of the target sequence by the guide
sequence.
The CRISPR/Cas or the CRISPR-Cas system utilizes a single Cas
enzyme that can be programmed by a short RNA molecule to recognize
a specific DNA target, in other words the Cas enzyme can be
recruited to a specific DNA target using said short RNA molecule.
In certain aspects, e.g., when not mutated or modified or when in a
native state, the Cas or CRISPR enzyme in CRISPR/Cas or the
CRISPR-Cas system, effects a cutting at a particular position; a
specific DNA target. Accordingly, data can be generated--a data
training set--relative to cutting by a CRISPR-Cas system at a
particular position in a nucleotide, e.g., DNA, sequence at a
particular position for a particular Cas or CRISPR enzyme.
Similarly, data can be generated--a data training set--relative to
cutting by a CRISPR-Cas system at a particular position in a
nucleotide, e.g., DNA, sequence of a particular mismatch of typical
nucleic acid hybridization (e.g., rather than G-C at particular
position, G-T or G-U or G-A or G-G) for the particular Cas. In
generating such data sets, there is the concept of average cutting
frequency. The frequency by which an enzyme will cut a nucleic acid
molecule, e.g., DNA, is mainly a function of the length of the
sequence it is sensitive to. For instance, if an enzyme has a
recognition sequence of 4 base-pairs, out of sheer probability,
with 4 positions, and each position having potentially 4 different
values, there are 4.sup.4 or 256 different possibilities for any
given 4-base long strand. Therefore, theoretically (assuming
completely random DNA), this enzyme will cut 1 in 256 4-base-pair
long sites. For an enzyme that recognizes a sequence of 6
base-pairs, the calculation is 4.sup.6 or 4096 possible
combinations with this length, and so such an enzyme will cut 1 in
4096 6-base-pair long sites. Of course, such calculations take into
consideration only that each position has potentially 4 different
values, and completely random DNA. However, DNA is not completely
random; for example, the G-C content of organisms varies.
Accordingly, the data training set(s) in the invention come from
observing cutting by a CRISPR-Cas system at a particular position
in a nucleotide, e.g., DNA, sequence at a particular position for a
particular Cas or CRISPR enzyme and observing cutting by a
CRISPR-Cas system at a particular position in a nucleotide, e.g.,
DNA, sequence of a particular mismatch of typical nucleic acid
hybridization for the particular Cas, in a statistically
significant number of experiments as to the particular position,
the CRISPR-Cas system and the particular Cas, and averaging the
results observed or obtained therefrom. The average cutting
frequency may be defined as the mean of the cleavage efficiencies
for all guide RNA:target DNA mismatches at a particular
location.
The invention further provides a method of identifying one or more
unique target sequences, e.g., in a genome, such as a genome of a
eukaryotic organism, whereby the target sequence is susceptible to
being recognized by a CRISPR-Cas system (and likewise, the
invention also further provides a method of identifying a
CRISPR-Cas system susceptible to recognizing one or more unique
target sequences), wherein the method comprises: a) determining
average cutting frequency at a particular position for a particular
Cas from a data training set as to that Cas, b) determining average
cutting frequency of a particular mismatch (e.g., guide-RNA/target
mismatch) for the particular Cas from the data training set, c)
multiplying the average cutting frequency at a particular position
by the average cutting frequency of a particular mismatch to obtain
a first product, d) repeating steps a) to c) to obtain second and
further products for any further particular position (s) of
mismatches and particular mismatches and multiplying those second
and further products by the first product, for an ultimate product,
and omitting this step if there is no mismatch at any position or
if there is only one particular mismatch at one particular position
(or optionally d) repeating steps a) to c) to obtain second and
further products for any further particular position (s) of
mismatches and particular mismatches and multiplying those second
and further products by the first product, for an ultimate product,
and omitting this step if there is no mismatch at any position or
if there is only one particular mismatch at one particular
position), and e) multiplying the ultimate product by the result of
dividing the minimum distance between consecutive mismatches by the
distance, in bp, between the first and last base of the target
sequence, e.g., 15-20, such as 18, and omitting this step if there
is no mismatch at any position or if there is only one particular
mismatch at one particular position (or optionally e) multiplying
the ultimate product by the result of dividing the minimum distance
between consecutive mismatches by the distance, in bp, between the
first and last base of the target sequence, e.g., 15-20, such as 18
and omitting this step if there is no mismatch at any position or
if there is only one particular mismatch at one particular
position), to thereby obtain a ranking, which allows for the
identification of one or more unique target sequences, to thereby
obtain a ranking, which allows for the identification of one or
more unique target sequences. Steps (a) and (b) can be performed in
either order. If there are no other products than the first
product, that first product (of step (c) from multiplying (a) times
(b)) is what is used to determine or obtain the ranking.
The invention also comprehends method of identifying one or more
unique target sequences in a genome of a eukaryotic organism,
whereby the target sequence is susceptible to being recognized by a
CRISPR-Cas system, wherein the method comprises: a) creating a data
training set as to a particular Cas, b) determining average cutting
frequency at a particular position for the particular Cas from the
data training set, c) determining average cutting frequency of a
particular mismatch for the particular Cas from the data training
set, d) multiplying the average cutting frequency at a particular
position by the average cutting frequency of a particular mismatch
to obtain a first product, e) repeating steps b) to d) to obtain
second and further products for any further particular position (s)
of mismatches and particular mismatches and multiplying those
second and further products by the first product, for an ultimate
product, and omitting this step if there is no mismatch at any
position or if there is only one particular mismatch at one
particular position (or optionally e) repeating steps b) to d) to
obtain second and further products for any further particular
position (s) of mismatches and particular mismatches and
multiplying those second and further products by the first product,
for an ultimate product, and omitting this step if there is no
mismatch at any position or if there is only one particular
mismatch at one particular position), and f) multiplying the
ultimate product by the result of dividing the minimum distance
between consecutive mismatches by 18 and omitting this step if
there is no mismatch at any position or if there is only one
particular mismatch at one particular position (or optionally f)
multiplying the ultimate product by the result of dividing the
minimum distance between consecutive mismatches by the distance, in
bp, between the first and last base of the target sequence, e.g.,
15-20, such as 18, and omitting this step if there is no mismatch
at any position or if there is only one particular mismatch at one
particular position), to thereby obtain a ranking, which allows for
the identification of one or more unique target sequences. Steps
(a) and (b) can be performed in either order. Steps (a) and (b) can
be performed in either order. If there are no other products than
the first product, that first product (of step (c) from multiplying
(a) times (b)) is what is used to determine or obtain the
ranking.
The invention also comprehends a method of identifying one or more
unique target sequences in a genome of a eukaryotic organism,
whereby the target sequence is susceptible to being recognized by a
CRISPR-Cas system, wherein the method comprises: a) determining
average cutting frequency of guide-RNA/target mismatches at a
particular position for a particular Cas from a training data set
as to that Cas, and/or b) determining average cutting frequency of
a particular mismatch-type for the particular Cas from the training
data set, to thereby obtain a ranking, which allows for the
identification of one or more unique target sequences. The method
may comprise determining both the average cutting frequency of
guide-RNA/target mismatches at a particular position for a
particular Cas from a training data set as to that Cas, and the
average cutting frequency of a particular mismatch-type for the
particular Cas from the training data set. Where both are
determined, the method may further comprise multiplying the average
cutting frequency at a particular position by the average cutting
frequency of a particular mismatch-type to obtain a first product,
repeating the determining and multiplying steps to obtain second
and further products for any further particular position(s) of
mismatches and particular mismatches and multiplying those second
and further products by the first product, for an ultimate product,
and omitting this step if there is no mismatch at any position or
if there is only one particular mismatch at one particular
position, and multiplying the ultimate product by the result of
dividing the minimum distance between consecutive mismatches by the
distance, in bp, between the first and last base of the target
sequence and omitting this step if there is no mismatch at any
position or if there is only one particular mismatch at one
particular position, to thereby obtain a ranking, which allows for
the identification of one or more unique target sequences. The
distance, in bp, between the first and last base of the target
sequence may be 18. The method may comprise creating a training set
as to a particular Cas. The method may comprise determining the
average cutting frequency of guide-RNA/target mismatches at a
particular position for a particular Cas from a training data set
as to that Cas, if more than one mismatch, repeating the
determining step so as to determine cutting frequency for each
mismatch, and multiplying frequencies of mismatches to thereby
obtain a ranking, which allows for the identification of one or
more unique target sequences.
The invention further comprehends a method of identifying one or
more unique target sequences in a genome of a eukaryotic organism,
whereby the target sequence is susceptible to being recognized by a
CRISPR-Cas system, wherein the method comprises: a) determining
average cutting frequency of guide-RNA/target mismatches at a
particular position for a particular Cas from a training data set
as to that Cas, and average cutting frequency of a particular
mismatch-type for the particular Cas from the training data set, to
thereby obtain a ranking, which allows for the identification of
one or more unique target sequences. The invention additionally
comprehends a method of identifying one or more unique target
sequences in a genome of a eukaryotic organism, whereby the target
sequence is susceptible to being recognized by a CRISPR-Cas system,
wherein the method comprises: a) creating a training data set as to
a particular Cas, b) determining average cutting frequency of
guide-RNA/target mismatches at a particular position for the
particular Cas from the training data set, and/or c) determining
average cutting frequency of a particular mismatch-type for the
particular Cas from the training data set, to thereby obtain a
ranking, which allows for the identification of one or more unique
target sequences. The invention yet further comprehends a method of
identifying one or more unique target sequences in a genome of a
eukaryotic organism, whereby the target sequence is susceptible to
being recognized by a CRISPR-Cas system, wherein the method
comprises: a) creating a training data set as to a particular Cas,
b) determining average cutting frequency of guide-RNA/target
mismatches at a particular position for the particular Cas from the
training data set, and average cutting frequency of a particular
mismatch-type for the particular Cas from the training data set, to
thereby obtain a ranking, which allows for the identification of
one or more unique target sequences. Accordingly, in these
embodiments, instead of multiplying cutting-frequency averages
uniquely determined for a mismatch position and mismatch type
separately, the invention uses averages that are uniquely
determined, e.g., cutting-frequency averages for a particular
mismatch type at a particular position (thereby without multiplying
these, as part of preparation of training set). These methods can
be performed iteratively akin to the steps in methods including
multiplication, for determination of one or more unique target
sequences.
The invention in certain aspects provides a method for selecting a
CRISPR complex for targeting and/or cleavage of a candidate target
nucleic acid sequence within a cell, comprising the steps of: (a)
determining amount, location and nature of mismatch(es) of guide
sequence of potential CRISPR complex(es) and the candidate target
nucleic acid sequence, (b) determining contribution of each of the
amount, location and nature of mismatch(es) to hybridization free
energy of binding between the target nucleic acid sequence and the
guide sequence of potential CRISPR complex(es) from a training data
set, (c) based on the contribution analysis of step (b), predicting
cleavage at the location(s) of the mismatch(es) of the target
nucleic acid sequence by the potential CRISPR complex(es), and (d)
selecting the CRISPR complex from potential CRISPR complex(es)
based on whether the prediction of step (c) indicates that it is
more likely than not that cleavage will occur at location(s) of
mismatch(es) by the CRISPR complex Step (b) may be performed by:
determining local thermodynamic contributions, .DELTA.G.sub.ij(k),
between every guide sequence i and target nucleic acid sequence j
at position k, wherein .DELTA.G.sub.ij(k) is estimated from a
biochemical prediction algorithm and .alpha..sub.k is a
position-dependent weight calculated from the training data set,
estimating values of the effective free-energy Z.sub.ij using the
relationship p.sub.ij.varies.e.sup.-.beta.Z.sup.ij, wherein
p.sub.ij is measured cutting frequency by guide sequence i on
target nucleic acid sequence j and .beta. is a positive constant of
proportionality, determining position-dependent weights
.alpha..sub.k by fitting across spacer/target-pairs with the sum
across all N bases of the guide-sequence
.times..times..alpha..times..DELTA..times..times..function.
##EQU00001##
and wherein, step (c) is performed by determining the
position-dependent weights from the effective free-energy {right
arrow over (Z.sub.est)}=G{right arrow over (.alpha.)} between each
spacer and every potential target in the genome, and determining
estimated spacer-target cutting frequencies
p.sub.est.varies.e.sup.-.beta.Z.sup.est to thereby predict
cleavage. Beta is implicitly fit by fitting the values of alpha
(that are completely free to be multiplied--in the process of
fitting--by whichever constant is suitable for Z=sum(alpha*Delta
G).
The invention also comprehends the creation of a training data set.
A training data set is data of cutting frequency measurements,
obtained to maximize coverage and redundancy for possible mismatch
types and positions. There are advantageously two experimental
paradigms for generating a training data set. In one aspect,
generating a data set comprises assaying for Cas, e.g., Cas9,
cleavage at a constant target and mutating guide sequences. In
another aspect, generating a data set comprises assaying for Cas,
e.g., Cas9, cleavage using a constant guide sequence and testing
cleavage at multiple DNA targets. Further, the method can be
performed in at least two ways: in vivo (in cells, tissue, or
living animal) or in vitro (with a cell-free assay, using in vitro
transcribed guide RNA and Cas, e.g., Cas9 protein delivered either
by whole cell lysate or purified protein). Advantageously the
method is performed by assaying for cleavage at a constant target
with mismatched guide RNA in vivo in cell lines. Because the guide
RNA may be generated in cells as a transcript from a RNA polymerase
III promoter (e.g. U6) driving a DNA oligo, it may be expressed as
a PCR cassette and transfect the guide RNA directly (FIG. 24c)
along with CBh-driven Cas9 (PX165, FIG. 24c). By co-transfecting
Cas9 and a guide RNA with one or several mismatches relative to the
constant DNA target, one may assess cleavage at a constant
endogenous locus by a nuclease assay such as SURVEYOR nuclease
assay or next-generation deep sequencing. This data may be
collected for at least one or multiple targets within a loci of
interest, e.g., at least 1, at least 5, at least 10, at least 15 or
at least 20 targets from the human EMX1 locus. In this manner, a
data training set can be readily generated for any locus of
interest. Accordingly, there are at least two ways for generating a
data training set--in vivo (in cell lines or living animal) or in
vitro (with a cell-free assay, using in vitro transcribed guide RNA
and Cas, e.g., Cas9, protein delivered either by whole cell lysate
or purified protein). Also, the experimental paradigm can
differ--e.g. with mutated guide sequences or with a constant guide
and an oligo library of many DNA targets. These targeting
experiments can be done in vitro as well. The readout would simply
be running a gel on the result of the in vitro cleavage assay--the
results will be cleaved and uncleaved fractions. Alternatively or
additionally, these fractions can be gel-isolated and sequencing
adapters can be ligated prior to deep sequencing on these
populations.
The invention comprehends computer-readable medium comprising codes
that, upon execution by one or more processors, implements a herein
method. The invention further comprehends a computer system for
performing a herein method. The system can include I. a memory unit
configured to receive and/or store sequence information of the
genome; and II. one or more processors alone or in combination
programmed to perform the herein method, whereby the identification
of one or more unique target sequences is advantageously displayed
or transmitted. The eukaryotic organism can be selected from the
group consisting of Homo sapiens (human), Mus musculus (mouse),
Rattus norvegicus (rat), Danio rerio (zebrafish), Drosophila
melanogaster (fruit fly), Caenorhabditis elegans (roundworm), Sus
scrofa (pig) and Bos taurus (cow). The target sequence can be a DNA
sequence, and the mismatch(es) can be of RNA of the CRISPR complex
and the DNA.
The invention also entails a method for selecting a CRISPR complex
for targeting and/or cleavage of a candidate target nucleic acid
sequence, e.g., within a cell, comprising the steps of: (a)
determining amount, location and nature of mismatch(es) of
potential CRISPR complex(es) and the candidate target nucleic acid
sequence, (b) determining the contribution of the mismatch(es)
based on the amount and location of the mismatch(es), (c) based on
the contribution analysis of step (b), predicting cleavage at the
location(s) of the mismatch(es), and (d) selecting the CRISPR
complex from potential CRISPR complex(es) based on whether the
prediction of step (c) indicates that it is more likely than not
that cleavage will occur at location(s) of mismatch(es) by the
CRISPR complex. The cell can be from a eukaryotic organism as
herein discussed. The determining steps can be based on the results
or data of the data training set(s) in the invention that come from
observing cutting by a CRISPR-Cas system at a particular position
in a nucleotide, e.g., DNA, sequence at a particular position for a
particular Cas or CRISPR enzyme and observing cutting by a
CRISPR-Cas system at a particular position in a nucleotide, e.g.,
DNA, sequence of a particular mismatch of typical nucleic acid
hybridization for the particular Cas, in a statistically
significant number of experiments as to the particular position,
the CRISPR-Cas system and the particular Cas, and averaging the
results observed or obtained therefrom. Accordingly, for example,
if the data training set shows that at a particular position the
CRISPR-Cas system including a particular Cas is rather promiscuous,
i.e., there can be mismatches and cutting, the amount and location
may be one position, and nature of the mismatch between the CRISPR
complex and the candidate target nucleic acid sequence may be not
serious such that the contribution of the mismatch to failure to
cut/bind may be negligible and the prediction for cleavage may be
more likely than not that cleavage will occur, despite the
mismatch. Accordingly, it should be clear that the data training
set(s) are not generated in silico but are generated in the
laboratory, e.g., are from in vitro, ex vivo and/or in vivo
studies. The results from the laboratory work, e.g., from in vitro,
ex vivo and/or in vivo studies, are input into computer systems for
performing herein methods.
In the herein methods the candidate target sequence can be a DNA
sequence, and the mismatch(es) can be of RNA of potential CRISPR
complex(es) and the DNA. In aspects of the invention mentioned
herein, the amount of mismatches indicates the number of mismatches
in DNA: RNA base pairing between the DNA of the target sequence and
the RNA of the guide sequence. In aspects of the invention the
location of mismatches indicates the specific location along the
sequence occupied by the mismatch and if more than one mismatch is
present if the mismatches are concatenated or occur consecutively
or if they are separated by at least one of more residues. In
aspects of the invention the nature of mismatches indicates the
nucleotide type involved in the mismatched base pairing. Base pairs
are matched according to G-C and A-U Watson-Crick base pairing.
The invention further involves a method for predicting the
efficiency of cleavage at candidate target nucleic acid sequence,
e.g., within a target in a cell, by a CRISPR complex comprising the
steps of: (a) determining amount, location and nature of
mismatch(es) of the CRISPR complex and the candidate target nucleic
acid sequence, (b) determining the contribution of the mismatch(es)
based on the amount and location of the mismatch(es), and (c) based
on the contribution analysis of step (b), predicting whether
cleavage is more likely than not to occur at location(s) of
mismatch(es), and thereby predicting cleavage. As with other herein
methods, the candidate target sequence can be a DNA sequence, and
the mismatch(es) can be of RNA of the CRISPR complex and the DNA.
The cell can be from a eukaryotic organism as herein discussed.
The invention even further provides a method for selecting a
candidate target sequence, e.g., within a nucleic acid sequence,
e.g., in a cell, for targeting by a CRISPR complex, comprising the
steps of: determining the local thermodynamic contributions,
.DELTA.G.sub.ij(k), between every spacer i and target j at position
k, expressing an effective free-energy Z.sub.ij for each
spacer/target-pair as the sum
.times..times..alpha..times..DELTA..times..times..function.
##EQU00002## wherein .DELTA.G.sub.ij(k) is local thermodynamic
contributions, estimated from a biochemical prediction algorithm
and .alpha..sub.k is position-dependent weights, and estimating the
effective free-energy Z through the relationship
p.sub.ij.varies.e.sup.-.beta.Z.sup.ij wherein p.sub.ij is the
measured cutting frequency by spacer i on target j and .beta. is a
positive constant fit across the entire data-set, and estimating
the position-dependent weights .alpha..sub.k by fitting G{right
arrow over (.alpha.)}={right arrow over (Z)} such that each
spacer-target pair (i,j) corresponds to a row in the matrix G and
each position k in the spacer-target pairing corresponds to a
column in the same matrix, and estimating the effective free-energy
{right arrow over (Z.sub.est)}=G{right arrow over (.alpha.)}
between each spacer and every potential target in the genome by
using the fitted values .alpha..sub.k, and selecting, based on
calculated effective free-energy values, the candidate
spacer/target pair ij according to their specificity and/or the
efficiency, given the estimated spacer-target cutting frequencies
p.sub.est.varies.e.sup.-.beta.Z.sup.est. The cell can be from a
eukaryotic organism as herein discussed.
The invention includes a computer-readable medium comprising codes
that, upon execution by one or more processors, implements a method
for selecting a CRISPR complex for targeting and/or cleavage of a
candidate target nucleic acid, e.g., sequence within a cell,
comprising the steps of: (a) determining amount, location and
nature of mismatch(es) of potential CRISPR complex(es) and the
candidate target nucleic acid sequence, (b) determining the
contribution of the mismatch(es) based on the amount and location
of the mismatch(es), (c) based on the contribution analysis of step
(b), predicting cleavage at the location(s) of the mismatch(es),
and (d) selecting the CRISPR complex from potential CRISPR
complex(es) based on whether the prediction of step (c) indicates
that it is more likely than not that cleavage will occur at
location(s) of mismatch(es) by the CRISPR complex. The cell can be
from a eukaryotic organism as herein discussed.
Also, the invention involves computer systems for selecting a
CRISPR complex for targeting and/or cleavage of a candidate target
nucleic acid sequence, e.g., within a cell, the system comprising:
a. a memory unit configured to receive and/or store sequence
information of the candidate target nucleic acid sequence; and b.
one or more processors alone or in combination programmed to (a)
determine amount, location and nature of mismatch(es) of potential
CRISPR complex(es) and the candidate target nucleic acid sequence,
(b) determine the contribution of the mismatch(es) based on the
amount and location of the mismatch(es), (c) based on the
contribution analysis of step (b), predicting cleavage at the
location(s) of the mismatch(es), and (d) select the CRISPR complex
from potential CRISPR complex(es) based on whether the prediction
of step (c) indicates that it is more likely than not that cleavage
will occur at location(s) of mismatch(es) by the CRISPR complex.
The cell can be from a eukaryotic organism as herein discussed. The
system can display or transmit the selection.
In aspects of the invention mentioned herein, the amount of
mismatches indicates the number of mismatches in DNA: RNA base
pairing between the DNA of the target sequence and the RNA of the
guide sequence. In aspects of the invention the location of
mismatches indicates the specific location along the sequence
occupied by the mismatch and if more than one mismatch is present
if the mismatches are concatenated or occur consecutively or if
they are separated by at least one of more residues. In aspects of
the invention the nature of mismatches indicates the nucleotide
type involved in the mismatched base pairing. Base pairs are
matched according to G-C and A-U Watson-Crick base pairing.
Accordingly, aspects of the invention relate to methods and
compositions used to determine the specificity of Cas9. In one
aspect the position and number of mismatches in the guide RNA is
tested against cleavage efficiency. This information enables the
design of target sequences that have minimal off-target
effects.
The invention also comprehends a method of identifying one or more
unique target sequences in a genome of a eukaryotic organism,
whereby the target sequence is susceptible to being recognized by a
CRISPR-Cas system, wherein the method comprises a) determining
average cutting frequency of guide-RNA/target mismatches at a
particular position for a particular Cas from a training data set
as to that Cas, and if more than one mismatch is present then step
a) is repeated so as to determine cutting frequency for each
mismatch after which frequencies of mismatches are multiplied to
thereby obtain a ranking, which allows for the identification of
one or more unique target sequences. The invention further
comprehends a method of identifying one or more unique target
sequences in a genome of a eukaryotic organism, whereby the target
sequence is susceptible to being recognized by a CRISPR-Cas system,
wherein the method comprises a) creating a training data set as to
a particular Cas, b) determining average cutting frequency of
guide-RNA/target mismatches at a particular position for a
particular Cas from the training data set, if more than one
mismatch exists, repeat step b) so as to determine cutting
frequency for each mismatch, then multiply frequencies of
mismatches to thereby obtain a ranking, which allows for the
identification of one or more unique target sequences. The
invention also relates to computer systems and computer readable
media that executes these methods.
In various aspects, the invention involves a computer system for
selecting a candidate target sequence within a nucleic acid
sequence or for selecting a Cas for a candidate target sequence,
e.g., selecting a target in a eukaryotic cell for targeting by a
CRISPR complex.
The computer system may comprise: (a) a memory unit configured to
receive and/or store said nucleic acid sequence; and (b) one or
more processors alone or in combination programmed to perform as
herein discussed. For example, programmed to: (i) locate a CRISPR
motif sequence (e.g., PAM) within said nucleic acid sequence, and
(ii) select a sequence adjacent to said located CRISPR motif
sequence (e.g. PAM) as the candidate target sequence to which the
CRISPR complex binds. In some embodiments, said locating step may
comprise identifying a CRISPR motif sequence (e.g. PAM) located
less than about 10000 nucleotides away from said target sequence,
such as less than about 5000, 2500, 1000, 500, 250, 100, 50, 25, or
fewer nucleotides away from the target sequence. In some
embodiments, the candidate target sequence is at least 10, 15, 20,
25, 30, or more nucleotides in length. In some embodiments the
candidate target sequence is 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
36, 37, 38, 39 or 40 nucleotides in length. In some embodiments,
the nucleotide at the 3' end of the candidate target sequence is
located no more than about 10 nucleotides upstream of the CRISPR
motif sequence (e.g. PAM), such as no more than 5, 4, 3, 2, or 1
nucleotides. In some embodiments, the nucleic acid sequence in the
eukaryotic cell is endogenous to the cell or organism, e.g.,
eukaryotic genome. In some embodiments, the nucleic acid sequence
in the eukaryotic cell is exogenous to the cell or organism, e.g.,
eukaryotic genome.
In various aspects, the invention provides a computer-readable
medium comprising codes that, upon execution by one or more
processors, implements a method described herein, e.g., of
selecting a candidate target sequence within a nucleic acid
sequence or selecting a CRISPR candidate for a target sequence; for
instance, a target sequence in a cell such as in a eukaryotic cell
for targeting by a CRISPR complex. The method can comprise: (i)
locate a CRISPR motif sequence (e.g., PAM) within said nucleic acid
sequence, and (ii) select a sequence adjacent to said located
CRISPR motif sequence (e.g. PAM) as the candidate target sequence
to which the CRISPR complex binds. In some embodiments, said
locating step may comprise identifying a CRISPR motif sequence
(e.g. PAM) located less than about 10000 nucleotides away from said
target sequence, such as less than about 5000, 2500, 1000, 500,
250, 100, 50, 25, or fewer nucleotides away from the target
sequence. In some embodiments, the candidate target sequence is at
least 10, 15, 20, 25, 30, or more nucleotides in length. In some
embodiments the candidate target sequence is 10, 11, 12, 13, 14,
15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
32, 33, 34, 35, 36, 37, 38, 39 or 40 nucleotides in length. In some
embodiments, the nucleotide at the 3' end of the candidate target
sequence is located no more than about 10 nucleotides upstream of
the CRISPR motif sequence (e.g. PAM), such as no more than 5, 4, 3,
2, or 1 nucleotides. In some embodiments, the nucleic acid sequence
in the eukaryotic cell is endogenous to the cell or organism, e.g.,
eukaryotic genome. In some embodiments, the nucleic acid sequence
in the eukaryotic cell is exogenous to the cell or organism, e.g.,
eukaryotic genome.
A computer system (or digital device) may be used to receive,
transmit, display and/or store results, analyze the results, and/or
produce a report of the results and analysis. A computer system may
be understood as a logical apparatus that can read instructions
from media (e.g. software) and/or network port (e.g. from the
internet), which can optionally be connected to a server having
fixed media. A computer system may comprise one or more of a CPU,
disk drives, input devices such as keyboard and/or mouse, and a
display (e.g. a monitor). Data communication, such as transmission
of instructions or reports, can be achieved through a communication
medium to a server at a local or a remote location. The
communication medium can include any means of transmitting and/or
receiving data. For example, the communication medium can be a
network connection, a wireless connection, or an internet
connection. Such a connection can provide for communication over
the World Wide Web. It is envisioned that data relating to the
present invention can be transmitted over such networks or
connections (or any other suitable means for transmitting
information, including but not limited to mailing a physical
report, such as a print-out) for reception and/or for review by a
receiver. The receiver can be but is not limited to an individual,
or electronic system (e.g. one or more computers, and/or one or
more servers).
In some embodiments, the computer system comprises one or more
processors. Processors may be associated with one or more
controllers, calculation units, and/or other units of a computer
system, or implanted in firmware as desired. If implemented in
software, the routines may be stored in any computer readable
memory such as in RAM, ROM, flash memory, a magnetic disk, a laser
disk, or other suitable storage medium. Likewise, this software may
be delivered to a computing device via any known delivery method
including, for example, over a communication channel such as a
telephone line, the internet, a wireless connection, etc., or via a
transportable medium, such as a computer readable disk, flash
drive, etc. The various steps may be implemented as various blocks,
operations, tools, modules and techniques which, in turn, may be
implemented in hardware, firmware, software, or any combination of
hardware, firmware, and/or software. When implemented in hardware,
some or all of the blocks, operations, techniques, etc. may be
implemented in, for example, a custom integrated circuit (IC), an
application specific integrated circuit (ASIC), a field
programmable logic array (FPGA), a programmable logic array (PLA),
etc.
A client-server, relational database architecture can be used in
embodiments of the invention. A client-server architecture is a
network architecture in which each computer or process on the
network is either a client or a server. Server computers are
typically powerful computers dedicated to managing disk drives
(file servers), printers (print servers), or network traffic
(network servers). Client computers include PCs (personal
computers) or workstations on which users run applications, as well
as example output devices as disclosed herein. Client computers
rely on server computers for resources, such as files, devices, and
even processing power. In some embodiments of the invention, the
server computer handles all of the database functionality. The
client computer can have software that handles all the front-end
data management and can also receive data input from users.
A machine readable medium comprising computer-executable code may
take many forms, including but not limited to, a tangible storage
medium, a carrier wave medium or physical transmission medium.
Non-volatile storage media include, for example, optical or
magnetic disks, such as any of the storage devices in any
computer(s) or the like, such as may be used to implement the
databases, etc. shown in the drawings. Volatile storage media
include dynamic memory, such as main memory of such a computer
platform. Tangible transmission media include coaxial cables;
copper wire and fiber optics, including the wires that comprise a
bus within a computer system. Carrier-wave transmission media may
take the form of electric or electromagnetic signals, or acoustic
or light waves such as those generated during radio frequency (RF)
and infrared (IR) data communications. Common forms of
computer-readable media therefore include for example: a floppy
disk, a flexible disk, hard disk, magnetic tape, any other magnetic
medium, a CD-ROM, DVD or DVD-ROM, any other optical medium, punch
cards paper tape, any other physical storage medium with patterns
of holes, a RAM, a ROM, a PROM and EPROM, a FLASH-EPROM, any other
memory chip or cartridge, a carrier wave transporting data or
instructions, cables or links transporting such a carrier wave, or
any other medium from which a computer may read programming code
and/or data. Many of these forms of computer readable media may be
involved in carrying one or more sequences of one or more
instructions to a processor for execution.
The subject computer-executable code can be executed on any
suitable device comprising a processor, including a server, a PC,
or a mobile device such as a smartphone or tablet. Any controller
or computer optionally includes a monitor, which can be a cathode
ray tube ("CRT") display, a flat panel display (e.g., active matrix
liquid crystal display, liquid crystal display, etc.), or others.
Computer circuitry is often placed in a box, which includes
numerous integrated circuit chips, such as a microprocessor,
memory, interface circuits, and others. The box also optionally
includes a hard disk drive, a floppy disk drive, a high capacity
removable drive such as a writeable CD-ROM, and other common
peripheral elements. Inputting devices such as a keyboard, mouse,
or touch-sensitive screen, optionally provide for input from a
user. The computer can include appropriate software for receiving
user instructions, either in the form of user input into a set of
parameter fields, e.g., in a GUI, or in the form of preprogrammed
instructions, e.g., preprogrammed for a variety of different
specific operations.
Accordingly, it is an object of the invention to not encompass
within the invention any previously known product, process of
making the product, or method of using the product such that
Applicants reserve the right and hereby disclose a disclaimer of
any previously known product, process, or method. It is further
noted that the invention does not intend to encompass within the
scope of the invention any product, process, or making of the
product or method of using the product, which does not meet the
written description and enablement requirements of the USPTO (35
U.S.C. .sctn. 112, first paragraph) or the EPO (Article 83 of the
EPC), such that Applicants reserve the right and hereby disclose a
disclaimer of any previously described product, process of making
the product, or method of using the product.
It is noted that in this disclosure and particularly in the claims
and/or paragraphs, terms such as "comprises", "comprised",
"comprising" and the like can have the meaning attributed to it in
U.S. Patent law; e.g., they can mean "includes", "included",
"including", and the like; and that terms such as "consisting
essentially of" and "consists essentially of" have the meaning
ascribed to them in U.S. Patent law, e.g., they allow for elements
not explicitly recited, but exclude elements that are found in the
prior art or that affect a basic or novel characteristic of the
invention.
These and other embodiments are disclosed or are obvious from and
encompassed by, the following Detailed Description.
BRIEF DESCRIPTION OF THE DRAWINGS
The novel features of the invention are set forth with
particularity in the appended claims. A better understanding of the
features and advantages of the present invention will be obtained
by reference to the following detailed description that sets forth
illustrative embodiments, in which the principles of the invention
are utilized, and the accompanying drawings of which:
FIG. 1 shows a schematic of RNA-guided Cas9 nuclease. The Cas9
nuclease from Streptococcus pyogenes is targeted to genomic DNA by
a synthetic guide RNA (sgRNA) consisting of a 20-nt guide sequence
and a scaffold. The guide sequence base-pairs with the DNA target,
directly upstream of a requisite 5'-NGG protospacer adjacent motif
(PAM; magenta), and Cas9 mediates a double-stranded break (DSB)
.about.3 bp upstream of the PAM (indicated by triangle).
FIG. 2A-F shows an exemplary CRISPR system and a possible mechanism
of action (A), an example adaptation for expression in eukaryotic
cells, and results of tests assessing nuclear localization and
CRISPR activity (B-F).
FIG. 3 shows a schematic representation assay carried out to
evaluate the cleavage specificity of Cas9 form Streptococcus
pyogenes. Single base pair mismatches between the guide RNA
sequence and the target DNA are mapped against cleavage efficiency
in %.
FIG. 4 shows a mapping of mutations in the PAM sequence to cleavage
efficiency in %.
FIG. 5A-C shows histograms of distances between adjacent S.
pyogenes SF370 locus 1 PAM (NGG) (FIG. 5A) and S. thermophilus LMD9
locus 2 PAM (NNAGAAW) (FIG. 5B) in the human genome; and distances
for each PAM by chromosome (Chr) (FIG. 5C).
FIG. 6A-C shows the graphing of distribution of distances between
NGG and NRG motifs in the human genome in an "overlapping"
fashion.
FIG. 7A-D shows a circular depiction of the phylogenetic analysis
revealing five families of Cas9s, including three groups of large
Cas9s (.about.1400 amino acids) and two of small Cas9s (.about.1100
amino acids).
FIG. 8A-F shows a linear depiction of the phylogenetic analysis
revealing five families of Cas9s, including three groups of large
Cas9s (.about.1400 amino acids) and two of small Cas9s (.about.1100
amino acids).
FIG. 9A-G shows the optimization of guide RNA architecture for
SpCas9-mediated mammalian genome editing. (a) Schematic of
bicistronic expression vector (PX330) for U6 promoter-driven single
guide RNA (sgRNA) and CBh promoter-driven human codon-optimized
Streptococcus pyogenes Cas9 (hSpCas9) used for all subsequent
experiments. The sgRNA consists of a 20-nt guide sequence (blue)
and scaffold (red), truncated at various positions as indicated.
(b) SURVEYOR assay for SpCas9-mediated indels at the human EMX1 and
PVALB loci. Arrows indicate the expected SURVEYOR fragments (n=3).
(c) Northern blot analysis for the four sgRNA truncation
architectures, with UI as loading control. (d) Both wildtype (wt)
or nickase mutant (D10A) of SpCas9 promoted insertion of a HindIII
site into the human EMX1 gene. Single stranded oligonucleotides
(ssODNs), oriented in either the sense or antisense direction
relative to genome sequence, were used as homologous recombination
templates (FIG. 68). (e) Schematic of the human SERPINB5 locus.
sgRNAs and PAMs are indicated by colored bars above sequence;
methylcytosine (Me) are highlighted (pink) and numbered relative to
the transcriptional start site (TSS, +1). (f) Methylation status of
SERPINB5 assayed by bisulfite sequencing of 16 clones. Filled
circles, methylated CpG; open circles, unmethylated CpG. (g)
Modification efficiency by three sgRNAs targeting the methylated
region of SERPINB5, assayed by deep sequencing (n=2). Error bars
indicate Wilson intervals.
FIG. 10A-C shows position, distribution, number and
mismatch-identity of some mismatch guide RNAs that can be used in
generating the data training set (study on off target Cas9
activity).
FIG. 11A-B shows further positions, distributions, numbers and
mismatch-identities of some mismatch guide RNAs that can be used in
generating the data training set (study on off target Cas9
activity).
FIG. 12A-E shows guide RNA single mismatch cleavage efficiency. a,
Multiple target sites were selected from the human EMX1 locus.
Individual bases at positions 1-19 along the guide RNA sequence,
which complementary to the target DNA sequence, were mutated to
every ribonucleotide mismatch from the original guide RNA (blue
`N`). b, On-target Cas9 cleavage activity for guide RNAs containing
single base mutations (light blue: high cutting, dark blue: low
cutting) relative to the on-target guide RNA (grey). c, Base
transition heat map representing relative Cas9 cleavage activity
for each possible RNA:DNA base pair. Rows were sorted based on
cleavage activity in the PAM-proximal 10 bases of the guide RNA
(high to low). Mean cleavage levels were calculated across base
transitions in the PAM-proximal 10 bases (right bar) and across all
transitions at each position (bottom bar). Heat map represents
aggregate single-base mutation data from 15 EMX1 targets. d, Mean
Cas9 locus modification efficiency at targets with all possible PAM
sequences. e, Histogram of distances between 5'-NRG PAM occurrences
within the human genome. Putative targets were identified using
both the plus and minus strand of human chromosomal sequences.
FIG. 13A-C shows Cas9 on-target cleavage efficiency with multiple
guide RNA mismatches and genome-wide specificity. a, Cas9 targeting
efficiency with guide RNAs containing concatenated mismatches of 2
(top), 3 (middle), or 5 (bottom) consecutive bases for EMX1 targets
1 and 6. Rows represent different mutated guide RNAs and show the
identity of each nucleotide mutation (white cells; grey cells
denote unmutated bases). b, Cas9 was targeted with guide RNAs
containing 3 (top, middle) or 4 (bottom) mismatches (white cells)
separated by different numbers of unmutated bases (gray cells). c,
Cleavage activity at targeted EMX1 target loci (top bar) as well as
at candidate off-target genomic sites. Putative off-target loci
contained 1-3 individual base differences (white cells) compared to
the on-target loci.
FIG. 14A-B shows SpCas9 cleaves methylated targets in vitro. a,
Plasmid targets containing CpG dinucleotides are either left
unmethylated or methylated in vitro by M.SssI. Methyl-CpG in either
the target sequence or PAM are indicated. b, Cleavage of either
unmethylated or methylated targets 1 and 2 by SpCas9 cell
lysate.
FIG. 15 shows a UCSC Genome Browser track for identifying unique S.
pyogenes Cas9 target sites in the human genome. A list of unique
sites for the human, mouse, rat, zebrafish, fruit fly, and C.
elegans genomes have been computationally identified and converted
into tracks that can be visualized using the UCSC genome browser.
Unique sites are defined as those sites with seed sequences
(3'-most 12 nucleotides of the spacer sequence plus the NGG PAM
sequence) that are unique in the entire genome.
FIG. 16 shows a UCSC Genome Browser track for identifying unique S.
pyogenes Cas9 target sites in the mouse genome.
FIG. 17 shows a UCSC Genome Browser track for identifying unique S.
pyogenes Cas9 target sites in the rat genome.
FIG. 18 shows a UCSC Genome Browser track for identifying unique S.
pyogenes Cas9 target sites in the zebra fish genome.
FIG. 19 shows a UCSC Genome Browser track for identifying unique S.
pyogenes Cas9 target sites in the D. melanogaster genome.
FIG. 20 shows a UCSC Genome Browser track for identifying unique S.
pyogenes Cas9 target sites in the C. elegans genome.
FIG. 21 shows a UCSC Genome Browser track for identifying unique S.
pyogenes Cas9 target sites in the pig genome.
FIG. 22 shows a UCSC Genome Browser track for identifying unique S.
pyogenes Cas9 target sites in the cow genome.
FIG. 23 shows CRISPR Designer, a web app for the identification of
Cas9 target sites. Most target regions (such as exons) contain
multiple possible CRISPR sgRNA+PAM sequences. To minimize predicted
off-targeted cleavage across the genome, a web-based computational
pipeline ranks all possible sgRNA sites by their predicted
genome-wide specificity and generates primers and oligos required
for construction of each possible CRISPR as well as primers (via
Primer3) for high-throughput assay of potential off-target cleavage
in a next-generation sequencing experiment. Optimization of the
choice of sgRNA within a user's target sequence: The goal is to
minimize total off-target activity across the human genome. For
each possible sgRNA choice, there is identification of off-target
sequences (preceding either NAG or NGG PAMs) across the human
genome that contain up to 5 mismatched base-pairs. The cleavage
efficiency at each off-target sequence is predicted using an
experimentally-derived weighting scheme. Each possible sgRNA is
then ranked according to its total predicted off-target cleavage;
the top-ranked sgRNAs represent those that are likely to have the
greatest on-target and the least off-target cleavage. In addition,
automated reagent design for CRISPR construction, primer design for
the on-target SURVEYOR assay, and primer design for high-throughput
detection and quantification of off-target cleavage via next-gen
sequencing are advantageously facilitated.
FIG. 24A-C shows Target selection and reagent preparation. (a) For
S. pyogenes Cas9, 20-bp targets (highlighted in blue) must be
followed by 5'-NGG, which can occur in either strand on genomic
DNA. (b) Schematic for co-transfection of Cas9 expression plasmid
(PX165) and PCR-amplified U6-driven sgRNA expression cassette.
Using a U6 promoter-containing PCR template and a fixed forward
primer (U6 Fwd), sgRNA-encoding DNA can appended onto the U6
reverse primer (U6 Rev) and synthesized as an extended DNA oligo
(Ultramer oligos from IDT). Note the guide sequence (blue N's) in
U6 Rev is the reverse complement of the 5'-NGG flanking target
sequence. (c) Schematic for scarless cloning of the guide sequence
oligos into a plasmid containing Cas9 and sgRNA scaffold (PX330).
The guide oligos (blue N's) contain overhangs for ligation into the
pair of BbsI sites on PS330, with the top and bottom strand
orientations matching those of the genomic target (i.e. top oligo
is the 20-bp sequence preceding 5'-NGG in genomic DNA). Digestion
of PX330 with BbsI allows the replacement of the Type IIs
restriction sites (blue outline) with direct insertion of annealed
oligos. It is worth noting that an extra G was placed before the
first base of the guide sequence. Applicants have found that an
extra G in front of the guide sequence does not adversely affect
targeting efficiency. In cases when the 20-nt guide sequence of
choice does not begin with guanine, the extra guanine will ensure
the sgRNA is efficiently transcribed by the U6 promoter, which
prefers a guanine in the first base of the transcript.
FIG. 25A-E shows the single nucleotide specificity of SpCas9. (a)
Schematic of the experimental design. sgRNAs carrying all possible
single base-pair mismatches (blue Ns) throughout the guide sequence
were tested for each EMX1 target site (target site 1 shown as
example). (b) Heatmap representation of relative SpCas9 cleavage
efficiency by 57 single-mutated and 1 non-mutated sgRNA s each for
four EMX1 target sites. For each EMX1 target, the identities of
single base-pair substitutions are indicated on the left; original
guide sequence is shown above and highlighted in the heatmap (grey
squares). Modification efficiencies (increasing from white to dark
blue) are normalized to the original guide sequence. (c) Heatmap
for relative SpCas9 cleavage efficiency for each possible RNA:DNA
base pair, compiled from aggregate data from single-mismatch guide
RNAs for 15 EMX1 targets. Mean cleavage levels were calculated for
the 10 PAM-proximal bases (right bar) and across all substitutions
at each position (bottom bar); positions in grey were not covered
by the 469 single-mutated and 15 non-mutated sgRNAs tested. (d)
SpCas9-mediated indel frequencies at targets with all possible PAM
sequences, determined using the SURVEYOR nuclease assay. Two target
sites from the EMX1 locus were tested for each PAM (Table 4). (e)
Histogram of distances between 5'-NRG PAM occurrences within the
human genome. Putative targets were identified using both strands
of human chromosomal sequences (GRCh37/hg19).
FIG. 26A-C shows the multiple mismatch specificity of SpCas9. (a)
SpCas9 cleavage efficiency with guide RNAs containing a,
consecutive mismatches of 2, 3, or 5 bases, or (b, c) multiple
mismatches separated by different numbers of unmutated bases for
EMX1 targets 1, 2, 3, and 6. Rows represent each mutated guide RNA;
nucleotide substitutions are shown in white cells; grey cells
denote unmutated bases. All indel frequencies are absolute and
analyzed by deep sequencing from 2 biological replicas. Error bars
indicate Wilson intervals (Example 7, Methods and Materials)
FIG. 27A-D shows SpCas9-mediated indel frequencies at predicted
genomic off-target loci. (a and b) Cleavage levels at putative
genomic off-target loci containing 2 or 3 individual mismatches
(white cells) for EMX1 target 1 and target 3 are analyzed by deep
sequencing. List of off-target sites are ordered by median position
of mutations. Putative off-target sites with additional mutations
did not exhibit detectable indels (Table 4). The Cas9 dosage was
3.times.10-10 nmol/cell, with equimolar sgRNA delivery. Error bars
indicate Wilson intervals. (c and d) Indel frequencies for EMX1
targets 1 and 3 and selected off target loci (OT) as a function of
SpCas9 and sgRNA dosage, normalized to on-target cleavage at
highest transfection dosage (n=2). 400 ng to 10 ng of Cas9-sgRNA
plasmid corresponds to 7.1.times.10-10 to 1.8.times.10-11
nmol/cell. Cleavage specificity is measured as a ratio of on- to
off-target cleavage.
FIG. 28A-B shows the human EMX1 locus with target sites. Schematic
of the human EMX1 locus showing the location of 15 target DNA
sites, indicated by blue lines with corresponding PAM in
magenta.
FIG. 29A-B shows additional genomic off-target site analysis.
Cleavage levels at candidate genomic off-target loci (white cells)
for a, EMX1 target 2 and b, EMX1 target 6 were analyzed by deep
sequencing. All indel frequencies are absolute and analyzed by deep
sequencing from 2 biological replicates. Error bars indicate Wilson
confidence intervals
FIG. 30 shows predicted and observed cutting frequency-ranks among
genome-wide targets.
FIG. 31 shows that the PAM for Staphylococcus aureus sp. Aureus
Cas9 is NNGRR.
FIG. 32 shows a flow diagram as to locational methods of the
invention.
FIG. 33A-B shows a flow diagram as to thermodynamic methods of the
invention.
FIG. 34 shows a flow diagram as to multiplication methods of the
invention.
FIG. 35 shows a schematic block diagram of a computer system which
can be used to implement the methods described herein.
FIG. 36 shows a scatter plot of unfitted training data and testing
data.
FIG. 37 shows a scatter plot of fitted training data and testing
data.
The figures herein are for illustrative purposes only and are not
necessarily drawn to scale.
DETAILED DESCRIPTION OF THE INVENTION
The invention relates to the engineering and optimization of
systems, methods and compositions used for the control of gene
expression involving sequence targeting, such as genome
perturbation or gene-editing, that relate to the CRISPR/Cas system
and components thereof (FIGS. 1 and 2). In advantageous
embodiments, the Cas enzyme is Cas9.
The terms "polynucleotide", "nucleotide", "nucleotide sequence",
"nucleic acid" and "oligonucleotide" are used interchangeably. They
refer to a polymeric form of nucleotides of any length, either
deoxyribonucleotides or ribonucleotides, or analogs thereof.
Polynucleotides may have any three dimensional structure, and may
perform any function, known or unknown. The following are
non-limiting examples of polynucleotides: coding or non-coding
regions of a gene or gene fragment, loci (locus) defined from
linkage analysis, exons, introns, messenger RNA (mRNA), transfer
RNA, ribosomal RNA, short interfering RNA (siRNA), short-hairpin
RNA (shRNA), micro-RNA (miRNA), ribozymes, cDNA, recombinant
polynucleotides, branched polynucleotides, plasmids, vectors,
isolated DNA of any sequence, isolated RNA of any sequence, nucleic
acid probes, and primers. The term also encompasses
nucleic-acid-like structures with synthetic backbones, see, e.g.,
Eckstein, 1991; Baserga et al., 1992; Milligan, 1993; WO 97/03211;
WO 96/39154; Mata, 1997; Strauss-Soukup, 1997; and Samstag, 1996. A
polynucleotide may comprise one or more modified nucleotides, such
as methylated nucleotides and nucleotide analogs. If present,
modifications to the nucleotide structure may be imparted before or
after assembly of the polymer. The sequence of nucleotides may be
interrupted by non-nucleotide components. A polynucleotide may be
further modified after polymerization, such as by conjugation with
a labeling component.
As used herein the term "wild type" is a term of the art understood
by skilled persons and means the typical form of an organism,
strain, gene or characteristic as it occurs in nature as
distinguished from mutant or variant forms.
As used herein the term "variant" should be taken to mean the
exhibition of qualities that have a pattern that deviates from what
occurs in nature.
The terms "non-naturally occurring" or "engineered" are used
interchangeably and indicate the involvement of the hand of man.
The terms, when referring to nucleic acid molecules or polypeptides
mean that the nucleic acid molecule or the polypeptide is at least
substantially free from at least one other component with which
they are naturally associated in nature and as found in nature.
"Complementarity" refers to the ability of a nucleic acid to form
hydrogen bond(s) with another nucleic acid sequence by either
traditional Watson-Crick or other non-traditional types. A percent
complementarity indicates the percentage of residues in a nucleic
acid molecule which can form hydrogen bonds (e.g., Watson-Crick
base pairing) with a second nucleic acid sequence (e.g., 5, 6, 7,
8, 9, 10 out of 10 being 50%, 60%, 70%, 80%, 90%, and 100%
complementary). "Perfectly complementary" means that all the
contiguous residues of a nucleic acid sequence will hydrogen bond
with the same number of contiguous residues in a second nucleic
acid sequence. "Substantially complementary" as used herein refers
to a degree of complementarity that is at least 60%, 65%, 70%, 75%,
80%, 85%, 90%, 95%, 97%, 98%, 99%, or 100% over a region of 8, 9,
10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30,
35, 40, 45, 50, or more nucleotides, or refers to two nucleic acids
that hybridize under stringent conditions.
As used herein, "stringent conditions" for hybridization refer to
conditions under which a nucleic acid having complementarity to a
target sequence predominantly hybridizes with the target sequence,
and substantially does not hybridize to non-target sequences.
Stringent conditions are generally sequence-dependent, and vary
depending on a number of factors. In general, the longer the
sequence, the higher the temperature at which the sequence
specifically hybridizes to its target sequence. Non-limiting
examples of stringent conditions are described in detail in Tijssen
(1993), Laboratory Techniques In Biochemistry And Molecular
Biology-Hybridization With Nucleic Acid Probes Part I, Second
Chapter "Overview of principles of hybridization and the strategy
of nucleic acid probe assay", Elsevier, N.Y.
"Hybridization" refers to a reaction in which one or more
polynucleotides react to form a complex that is stabilized via
hydrogen bonding between the bases of the nucleotide residues. The
hydrogen bonding may occur by Watson Crick base pairing, Hoogstein
binding, or in any other sequence specific manner. The complex may
comprise two strands forming a duplex structure, three or more
strands forming a multi stranded complex, a single self-hybridizing
strand, or any combination of these. A hybridization reaction may
constitute a step in a more extensive process, such as the
initiation of PCR, or the cleavage of a polynucleotide by an
enzyme. A sequence capable of hybridizing with a given sequence is
referred to as the "complement" of the given sequence.
As used herein, the term "genomic locus" or "locus" (plural loci)
is the specific location of a gene or DNA sequence on a chromosome.
A "gene" refers to stretches of DNA or RNA that encode a
polypeptide or an RNA chain that has functional role to play in an
organism and hence is the molecular unit of heredity in living
organisms. For the purpose of this invention it may be considered
that genes include regions which regulate the production of the
gene product, whether or not such regulatory sequences are adjacent
to coding and/or transcribed sequences. Accordingly, a gene
includes, but is not necessarily limited to, promoter sequences,
terminators, translational regulatory sequences such as ribosome
binding sites and internal ribosome entry sites, enhancers,
silencers, insulators, boundary elements, replication origins,
matrix attachment sites and locus control regions.
As used herein, "expression of a genomic locus" or "gene
expression" is the process by which information from a gene is used
in the synthesis of a functional gene product. The products of gene
expression are often proteins, but in non-protein coding genes such
as rRNA genes or tRNA genes, the product is functional RNA. The
process of gene expression is used by all known life--eukaryotes
(including multicellular organisms), prokaryotes (bacteria and
archaea) and viruses to generate functional products to survive. As
used herein "expression" of a gene or nucleic acid encompasses not
only cellular gene expression, but also the transcription and
translation of nucleic acid(s) in cloning systems and in any other
context. As used herein, "expression" also refers to the process by
which a polynucleotide is transcribed from a DNA template (such as
into and mRNA or other RNA transcript) and/or the process by which
a transcribed mRNA is subsequently translated into peptides,
polypeptides, or proteins. Transcripts and encoded polypeptides may
be collectively referred to as "gene product." If the
polynucleotide is derived from genomic DNA, expression may include
splicing of the mRNA in a eukaryotic cell.
The terms "polypeptide", "peptide" and "protein" are used
interchangeably herein to refer to polymers of amino acids of any
length. The polymer may be linear or branched, it may comprise
modified amino acids, and it may be interrupted by non amino acids.
The terms also encompass an amino acid polymer that has been
modified; for example, disulfide bond formation, glycosylation,
lipidation, acetylation, phosphorylation, or any other
manipulation, such as conjugation with a labeling component. As
used herein the term "amino acid" includes natural and/or unnatural
or synthetic amino acids, including glycine and both the D or L
optical isomers, and amino acid analogs and peptidomimetics.
As used herein, the term "domain" or "protein domain" refers to a
part of a protein sequence that may exist and function
independently of the rest of the protein chain.
As described in aspects of the invention, sequence identity is
related to sequence homology. Homology comparisons may be conducted
by eye, or more usually, with the aid of readily available sequence
comparison programs. These commercially available computer programs
may calculate percent (%) homology between two or more sequences
and may also calculate the sequence identity shared by two or more
amino acid or nucleic acid sequences. In some preferred
embodiments, the capping region of the dTALEs described herein have
sequences that are at least 95% identical or share identity to the
capping region amino acid sequences provided herein.
Sequence homologies may be generated by any of a number of computer
programs known in the art, for example BLAST or FASTA, etc. A
suitable computer program for carrying out such an alignment is the
GCG Wisconsin Bestfit package (University of Wisconsin, U.S.A;
Devereux et al., 1984, Nucleic Acids Research 12:387). Examples of
other software than may perform sequence comparisons include, but
are not limited to, the BLAST package (see Ausubel et al., 1999
ibid--Chapter 18), FASTA (Atschul et al., 1990, J. Mol. Biol.,
403-410) and the GENEWORKS suite of comparison tools. Both BLAST
and FASTA are available for offline and online searching (see
Ausubel et al., 1999 ibid, pages 7-58 to 7-60). However it is
preferred to use the GCG Bestfit program. % homology may be
calculated over contiguous sequences, i.e., one sequence is aligned
with the other sequence and each amino acid or nucleotide in one
sequence is directly compared with the corresponding amino acid or
nucleotide in the other sequence, one residue at a time. This is
called an "ungapped" alignment. Typically, such ungapped alignments
are performed only over a relatively short number of residues.
Although this is a very simple and consistent method, it fails to
take into consideration that, for example, in an otherwise
identical pair of sequences, one insertion or deletion may cause
the following amino acid residues to be put out of alignment, thus
potentially resulting in a large reduction in % homology when a
global alignment is performed. Consequently, most sequence
comparison methods are designed to produce optimal alignments that
take into consideration possible insertions and deletions without
unduly penalizing the overall homology or identity score. This is
achieved by inserting "gaps" in the sequence alignment to try to
maximize local homology or identity. However, these more complex
methods assign "gap penalties" to each gap that occurs in the
alignment so that, for the same number of identical amino acids, a
sequence alignment with as few gaps as possible--reflecting higher
relatedness between the two compared sequences--may achieve a
higher score than one with many gaps. "Affinity gap costs" are
typically used that charge a relatively high cost for the existence
of a gap and a smaller penalty for each subsequent residue in the
gap. This is the most commonly used gap scoring system. High gap
penalties may, of course, produce optimized alignments with fewer
gaps. Most alignment programs allow the gap penalties to be
modified. However, it is preferred to use the default values when
using such software for sequence comparisons. For example, when
using the GCG Wisconsin Bestfit package the default gap penalty for
amino acid sequences is -12 for a gap and -4 for each extension.
Calculation of maximum % homology therefore first requires the
production of an optimal alignment, taking into consideration gap
penalties. A suitable computer program for carrying out such an
alignment is the GCG Wisconsin Bestfit package (Devereux et al.,
1984 Nuc. Acids Research 12 p 387). Examples of other software than
may perform sequence comparisons include, but are not limited to,
the BLAST package (see Ausubel et al., 1999 Short Protocols in
Molecular Biology, 4th Ed.--Chapter 18), FASTA (Altschul et al.,
1990 J. Mol. Biol. 403-410) and the GENEWORKS suite of comparison
tools. Both BLAST and FASTA are available for offline and online
searching (see Ausubel et al., 1999, Short Protocols in Molecular
Biology, pages 7-58 to 7-60). However, for some applications, it is
preferred to use the GCG Bestfit program. A new tool, called BLAST
2 Sequences is also available for comparing protein and nucleotide
sequences (see FEMS Microbiol Lett. 1999 174(2): 247-50; FEMS
Microbiol Lett. 1999 177(1): 187-8 and the website of the National
Center for Biotechnology information at the website of the National
Institutes for Health). Although the final % homology may be
measured in terms of identity, the alignment process itself is
typically not based on an all-or-nothing pair comparison. Instead,
a scaled similarity score matrix is generally used that assigns
scores to each pair-wise comparison based on chemical similarity or
evolutionary distance. An example of such a matrix commonly used is
the BLOSUM62 matrix--the default matrix for the BLAST suite of
programs. GCG Wisconsin programs generally use either the public
default values or a custom symbol comparison table, if supplied
(see user manual for further details). For some applications, it is
preferred to use the public default values for the GCG package, or
in the case of other software, the default matrix, such as
BLOSUM62.
Alternatively, percentage homologies may be calculated using the
multiple alignment feature in DNASIS.TM. (Hitachi Software), based
on an algorithm, analogous to CLUSTAL (Higgins D G & Sharp P M
(1988), Gene 73(1), 237-244). Once the software has produced an
optimal alignment, it is possible to calculate % homology,
preferably % sequence identity. The software typically does this as
part of the sequence comparison and generates a numerical
result.
The sequences may also have deletions, insertions or substitutions
of amino acid residues which produce a silent change and result in
a functionally equivalent substance. Deliberate amino acid
substitutions may be made on the basis of similarity in amino acid
properties (such as polarity, charge, solubility, hydrophobicity,
hydrophilicity, and/or the amphipathic nature of the residues) and
it is therefore useful to group amino acids together in functional
groups. Amino acids may be grouped together based on the properties
of their side chains alone. However, it is more useful to include
mutation data as well. The sets of amino acids thus derived are
likely to be conserved for structural reasons. These sets may be
described in the form of a Venn diagram (Livingstone C. D. and
Barton G. J. (1993) "Protein sequence alignments: a strategy for
the hierarchical analysis of residue conservation" Comput. Appl.
Biosci. 9: 745-756) (Taylor W. R. (1986) "The classification of
amino acid conservation" J. Theor. Biol. 119; 205-218).
Conservative substitutions may be made, for example according to
the table below which describes a generally accepted Venn diagram
grouping of amino acids.
TABLE-US-00001 Set Sub-set Hydrophobic F W Y H K M Aromatic F W Y H
I L V A G C Aliphatic I L V Polar W Y H K R E Charged H K R E D D C
S T N Q Positively H K R charged Negatively E D charged Small V C A
G S P Tiny A G S T N D
Embodiments of the invention include sequences (both polynucleotide
or polypeptide) which may comprise homologous substitution
(substitution and replacement are both used herein to mean the
interchange of an existing amino acid residue or nucleotide, with
an alternative residue or nucleotide) that may occur i.e.,
like-for-like substitution in the case of amino acids such as basic
for basic, acidic for acidic, polar for polar, etc. Non-homologous
substitution may also occur i.e., from one class of residue to
another or alternatively involving the inclusion of unnatural amino
acids such as ornithine (hereinafter referred to as Z),
diaminobutyric acid ornithine (hereinafter referred to as B),
norleucine ornithine (hereinafter referred to as O), pyriylalanine,
thienylalanine, naphthylalanine and phenylglycine.
Variant amino acid sequences may include suitable spacer groups
that may be inserted between any two amino acid residues of the
sequence including alkyl groups such as methyl, ethyl or propyl
groups in addition to amino acid spacers such as glycine or
.beta.-alanine residues. A further form of variation, which
involves the presence of one or more amino acid residues in peptoid
form, may be well understood by those skilled in the art. For the
avoidance of doubt, "the peptoid form" is used to refer to variant
amino acid residues wherein the .alpha.-carbon substituent group is
on the residue's nitrogen atom rather than the .alpha.-carbon.
Processes for preparing peptides in the peptoid form are known in
the art, for example Simon R J et al., PNAS (1992) 89(20),
9367-9371 and Horwell D C, Trends Biotechnol. (1995) 13(4),
132-134.
The practice of the present invention employs, unless otherwise
indicated, conventional techniques of immunology, biochemistry,
chemistry, molecular biology, microbiology, cell biology, genomics
and recombinant DNA, which are within the skill of the art. See
Sambrook, Fritsch and Maniatis, MOLECULAR CLONING: A LABORATORY
MANUAL, 2nd edition (1989); CURRENT PROTOCOLS IN MOLECULAR BIOLOGY
(F. M. Ausubel, et al. eds., (1987)); the series METHODS IN
ENZYMOLOGY (Academic Press, Inc.): PCR 2: A PRACTICAL APPROACH (M.
J. MacPherson, B. D. Hames and G. R. Taylor eds. (1995)), Harlow
and Lane, eds. (1988) ANTIBODIES, A LABORATORY MANUAL, and ANIMAL
CELL CULTURE (R. I. Freshney, ed. (1987)).
In one aspect, the invention provides for vectors that are used in
the engineering and optimization of CRISPR/Cas systems. A used
herein, a "vector" is a tool that allows or facilitates the
transfer of an entity from one environment to another. It is a
replicon, such as a plasmid, phage, or cosmid, into which another
DNA segment may be inserted so as to bring about the replication of
the inserted segment. Generally, a vector is capable of replication
when associated with the proper control elements. In general, the
term "vector" refers to a nucleic acid molecule capable of
transporting another nucleic acid to which it has been linked.
Vectors include, but are not limited to, nucleic acid molecules
that are single-stranded, double-stranded, or partially
double-stranded; nucleic acid molecules that comprise one or more
free ends, no free ends (e.g. circular); nucleic acid molecules
that comprise DNA, RNA, or both; and other varieties of
polynucleotides known in the art. One type of vector is a
"plasmid," which refers to a circular double stranded DNA loop into
which additional DNA segments can be inserted, such as by standard
molecular cloning techniques. Another type of vector is a viral
vector, wherein virally-derived DNA or RNA sequences are present in
the vector for packaging into a virus (e.g. retroviruses,
replication defective retroviruses, adenoviruses, replication
defective adenoviruses, and adeno-associated viruses). Viral
vectors also include polynucleotides carried by a virus for
transfection into a host cell. Certain vectors are capable of
autonomous replication in a host cell into which they are
introduced (e.g. bacterial vectors having a bacterial origin of
replication and episomal mammalian vectors). Other vectors (e.g.,
non-episomal mammalian vectors) are integrated into the genome of a
host cell upon introduction into the host cell, and thereby are
replicated along with the host genome. Moreover, certain vectors
are capable of directing the expression of genes to which they are
operatively-linked. Such vectors are referred to herein as
"expression vectors." Common expression vectors of utility in
recombinant DNA techniques are often in the form of plasmids.
Recombinant expression vectors can comprise a nucleic acid of the
invention in a form suitable for expression of the nucleic acid in
a host cell, which means that the recombinant expression vectors
include one or more regulatory elements, which may be selected on
the basis of the host cells to be used for expression, that is
operatively-linked to the nucleic acid sequence to be expressed.
Within a recombinant expression vector, "operably linked" is
intended to mean that the nucleotide sequence of interest is linked
to the regulatory element(s) in a manner that allows for expression
of the nucleotide sequence (e.g. in an in vitro
transcription/translation system or in a host cell when the vector
is introduced into the host cell). With regards to recombination
and cloning methods, mention is made of U.S. patent application
Ser. No. 10/815,730, the contents of which are herein incorporated
by reference in their entirety.
Aspects of the invention can relate to bicistronic vectors for
chimeric RNA and Cas9. Cas9 is driven by the CBh promoter and the
chimeric RNA is driven by a U6 promoter. The chimeric guide RNA
consists of a 20 bp guide sequence (Ns) joined to the tracr
sequence (running from the first "U" of the lower strand to the end
of the transcript), which is truncated at various positions as
indicated. The guide and tracr sequences are separated by the
tracr-mate sequence GUUUUAGAGCUA followed by the loop sequence
GAAA. Results of SURVEYOR assays for Cas9-mediated indels at the
human EMX1 and PVALB loci are illustrated in FIGS. 16b and 16c,
respectively. Arrows indicate the expected SURVEYOR fragments.
ChiRNAs are indicated by their "+n" designation, and crRNA refers
to a hybrid RNA where guide and tracr sequences are expressed as
separate transcripts. Throughout this application, chimeric RNA
(chiRNA) may also be called single guide, or synthetic guide RNA
(sgRNA).
The term "regulatory element" is intended to include promoters,
enhancers, internal ribosomal entry sites (IRES), and other
expression control elements (e.g. transcription termination
signals, such as polyadenylation signals and poly-U sequences).
Such regulatory elements are described, for example, in Goeddel,
GENE EXPRESSION TECHNOLOGY: METHODS IN ENZYMOLOGY 185, Academic
Press, San Diego, Calif. (1990). Regulatory elements include those
that direct constitutive expression of a nucleotide sequence in
many types of host cell and those that direct expression of the
nucleotide sequence only in certain host cells (e.g.,
tissue-specific regulatory sequences). A tissue-specific promoter
may direct expression primarily in a desired tissue of interest,
such as muscle, neuron, bone, skin, blood, specific organs (e.g.
liver, pancreas), or particular cell types (e.g. lymphocytes).
Regulatory elements may also direct expression in a
temporal-dependent manner, such as in a cell-cycle dependent or
developmental stage-dependent manner, which may or may not also be
tissue or cell-type specific. In some embodiments, a vector
comprises one or more pol III promoter (e.g. 1, 2, 3, 4, 5, or more
pol I promoters), one or more pol II promoters (e.g. 1, 2, 3, 4, 5,
or more pol II promoters), one or more pol I promoters (e.g. 1, 2,
3, 4, 5, or more pol I promoters), or combinations thereof.
Examples of pol III promoters include, but are not limited to, U6
and H1 promoters. Examples of pol II promoters include, but are not
limited to, the retroviral Rous sarcoma virus (RSV) LTR promoter
(optionally with the RSV enhancer), the cytomegalovirus (CMV)
promoter (optionally with the CMV enhancer) [see, e.g., Boshart et
al, Cell, 41:521-530 (1985)], the SV40 promoter, the dihydrofolate
reductase promoter, the .beta.-actin promoter, the phosphoglycerol
kinase (PGK) promoter, and the EF1.alpha. promoter. Also
encompassed by the term "regulatory element" are enhancer elements,
such as WPRE; CMV enhancers; the R-U5' segment in LTR of HTLV-I
(Mol. Cell. Biol., Vol. 8(1), p. 466-472, 1988); SV40 enhancer; and
the intron sequence between exons 2 and 3 of rabbit .beta.-globin
(Proc. Natl. Acad. Sci. USA., Vol. 78(3), p. 1527-31, 1981). It
will be appreciated by those skilled in the art that the design of
the expression vector can depend on such factors as the choice of
the host cell to be transformed, the level of expression desired,
etc. A vector can be introduced into host cells to thereby produce
transcripts, proteins, or peptides, including fusion proteins or
peptides, encoded by nucleic acids as described herein (e.g.,
clustered regularly interspersed short palindromic repeats (CRISPR)
transcripts, proteins, enzymes, mutant forms thereof, fusion
proteins thereof, etc.). With regards to regulatory sequences,
mention is made of U.S. patent application Ser. No. 10/491,026, the
contents of which are incorporated by reference herein in their
entirety. With regards to promoters, mention is made of PCT
publication WO 2011/028929 and U.S. application Ser. No.
12/511,940, the contents of which are incorporated by reference
herein in their entirety.
Vectors can be designed for expression of CRISPR transcripts (e.g.
nucleic acid transcripts, proteins, or enzymes) in prokaryotic or
eukaryotic cells. For example, CRISPR transcripts can be expressed
in bacterial cells such as Escherichia coli, insect cells (using
baculovirus expression vectors), yeast cells, or mammalian cells.
Suitable host cells are discussed further in Goeddel, GENE
EXPRESSION TECHNOLOGY: METHODS IN ENZYMOLOGY 185, Academic Press,
San Diego, Calif. (1990). Alternatively, the recombinant expression
vector can be transcribed and translated in vitro, for example
using T7 promoter regulatory sequences and T7 polymerase. Vectors
may be introduced and propagated in a prokaryote or prokaryotic
cell. In some embodiments, a prokaryote is used to amplify copies
of a vector to be introduced into a eukaryotic cell or as an
intermediate vector in the production of a vector to be introduced
into a eukaryotic cell (e.g. amplifying a plasmid as part of a
viral vector packaging system). In some embodiments, a prokaryote
is used to amplify copies of a vector and express one or more
nucleic acids, such as to provide a source of one or more proteins
for delivery to a host cell or host organism. Expression of
proteins in prokaryotes is most often carried out in Escherichia
coli with vectors containing constitutive or inducible promoters
directing the expression of either fusion or non-fusion proteins.
Fusion vectors add a number of amino acids to a protein encoded
therein, such as to the amino terminus of the recombinant protein.
Such fusion vectors may serve one or more purposes, such as: (i) to
increase expression of recombinant protein; (ii) to increase the
solubility of the recombinant protein; and (iii) to aid in the
purification of the recombinant protein by acting as a ligand in
affinity purification. Often, in fusion expression vectors, a
proteolytic cleavage site is introduced at the junction of the
fusion moiety and the recombinant protein to enable separation of
the recombinant protein from the fusion moiety subsequent to
purification of the fusion protein. Such enzymes, and their cognate
recognition sequences, include Factor Xa, thrombin and
enterokinase. Example fusion expression vectors include pGEX
(Pharmacia Biotech Inc; Smith and Johnson, 1988. Gene 67: 31-40),
pMAL (New England Biolabs, Beverly, Mass.) and pRIT5 (Pharmacia,
Piscataway, N.J.) that fuse glutathione S-transferase (GST),
maltose E binding protein, or protein A, respectively, to the
target recombinant protein. Examples of suitable inducible
non-fusion E. coli expression vectors include pTrc (Amrann et al.,
(1988) Gene 69:301-315) and pET 11d (Studier et al., GENE
EXPRESSION TECHNOLOGY: METHODS IN ENZYMOLOGY 185, Academic Press,
San Diego, Calif. (1990) 60-89). In some embodiments, a vector is a
yeast expression vector. Examples of vectors for expression in
yeast Saccharomyces cerivisae include pYepSec1 (Baldari, et al.,
1987. EMBO J. 6: 229-234), pMFa (Kuijan and Herskowitz, 1982. Cell
30: 933-943), pJRY88 (Schultz et al., 1987. Gene 54: 113-123),
pYES2 (Invitrogen Corporation, San Diego, Calif.), and picZ
(InVitrogen Corp, San Diego, Calif.). In some embodiments, a vector
drives protein expression in insect cells using baculovirus
expression vectors. Baculovirus vectors available for expression of
proteins in cultured insect cells (e.g., SF9 cells) include the pAc
series (Smith, et al., 1983. Mol. Cell. Biol. 3: 2156-2165) and the
pVL series (Lucklow and Summers, 1989. Virology 170: 31-39). In
some embodiments, a vector is capable of driving expression of one
or more sequences in mammalian cells using a mammalian expression
vector. Examples of mammalian expression vectors include pCDM8
(Seed, 1987. Nature 329: 840) and pMT2PC (Kaufman, et al., 1987.
EMBO J. 6: 187-195). When used in mammalian cells, the expression
vector's control functions are typically provided by one or more
regulatory elements. For example, commonly used promoters are
derived from polyoma, adenovirus 2, cytomegalovirus, simian virus
40, and others disclosed herein and known in the art. For other
suitable expression systems for both prokaryotic and eukaryotic
cells see, e.g., Chapters 16 and 17 of Sambrook, et al., MOLECULAR
CLONING: A LABORATORY MANUAL. 2nd ed., Cold Spring Harbor
Laboratory, Cold Spring Harbor Laboratory Press, Cold Spring
Harbor, N.Y., 1989.
In some embodiments, the recombinant mammalian expression vector is
capable of directing expression of the nucleic acid preferentially
in a particular cell type (e.g., tissue-specific regulatory
elements are used to express the nucleic acid). Tissue-specific
regulatory elements are known in the art. Non-limiting examples of
suitable tissue-specific promoters include the albumin promoter
(liver-specific; Pinkert, et al., 1987. Genes Dev. 1: 268-277),
lymphoid-specific promoters (Calame and Eaton, 1988. Adv. Immunol.
43: 235-275), in particular promoters of T cell receptors (Winoto
and Baltimore, 1989. EMBO J. 8: 729-733) and immunoglobulins
(Baneiji, et al., 1983. Cell 33: 729-740; Queen and Baltimore,
1983. Cell 33: 741-748), neuron-specific promoters (e.g., the
neurofilament promoter; Byrne and Ruddle, 1989. Proc. Natl. Acad.
Sci. USA 86: 5473-5477), pancreas-specific promoters (Edlund, et
al., 1985. Science 230: 912-916), and mammary gland-specific
promoters (e.g., milk whey promoter; U.S. Pat. No. 4,873,316 and
European Application Publication No. 264,166).
Developmentally-regulated promoters are also encompassed, e.g., the
murine hox promoters (Kessel and Gruss, 1990. Science 249: 374-379)
and the .alpha.-fetoprotein promoter (Campes and Tilghman, 1989.
Genes Dev. 3: 537-546). With regard to these prokaryotic and
eukaryotic vectors, mention is made of U.S. Pat. No. 6,750,059, the
contents of which are incorporated by reference herein in their
entirety. Other embodiments of the invention may relate to the use
of viral vectors, with regards to which mention is made of U.S.
patent application Ser. No. 13/092,085, the contents of which are
incorporated by reference herein in their entirety. Tissue-specific
regulatory elements are known in the art and in this regard,
mention is made of U.S. Pat. No. 7,776,321, the contents of which
are incorporated by reference herein in their entirety.
In some embodiments, a regulatory element is operably linked to one
or more elements of a CRISPR system so as to drive expression of
the one or more elements of the CRISPR system. In general, CRISPRs
(Clustered Regularly Interspaced Short Palindromic Repeats), also
known as SPIDRs (SPacer Interspersed Direct Repeats), constitute a
family of DNA loci that are usually specific to a particular
bacterial species. The CRISPR locus comprises a distinct class of
interspersed short sequence repeats (SSRs) that were recognized in
E. coli (Ishino et al., J. Bacteriol., 169:5429-5433 [1987]; and
Nakata et al., J. Bacteriol., 171:3553-3556 [1989]), and associated
genes. Similar interspersed SSRs have been identified in Haloferax
mediterranei, Streptococcus pyogenes, Anabaena, and Mycobacterium
tuberculosis (See, Groenen et al., Mol. Microbiol., 10:1057-1065
[1993]; Hoe et al., Emerg. Infect. Dis., 5:254-263 [1999]; Masepohl
et al., Biochim. Biophys. Acta 1307:26-30 [1996]; and Mojica et
al., Mol. Microbiol., 17:85-93 [1995]). The CRISPR loci typically
differ from other SSRs by the structure of the repeats, which have
been termed short regularly spaced repeats (SRSRs) (Janssen et al.,
OMICS J. Integ. Biol., 6:23-33 [2002]; and Mojica et al., Mol.
Microbiol., 36:244-246 [2000]). In general, the repeats are short
elements that occur in clusters that are regularly spaced by unique
intervening sequences with a substantially constant length (Mojica
et al., [2000], supra). Although the repeat sequences are highly
conserved between strains, the number of interspersed repeats and
the sequences of the spacer regions typically differ from strain to
strain (van Embden et al., J. Bacteriol., 182:2393-2401 [2000]).
CRISPR loci have been identified in more than 40 prokaryotes (See
e.g., Jansen et al., Mol. Microbiol., 43:1565-1575 [2002]; and
Mojica et al., [2005]) including, but not limited to Aeropyrum,
Pyrobaculum, Sulfolobus, Archaeoglobus, Halocarcula,
Methanobacterium, Methanococcus, Methanosarcina, Methanopyrus,
Pyrococcus, Picrophilus, Thermoplasma, Corynebacterium,
Mycobacterium, Streptomyces, Aquifex, Porphyromonas, Chlorobium,
Thermus, Bacillus, Listeria, Staphylococcus, Clostridium,
Thermoanaerobacter, Mycoplasma, Fusobacterium, Azarcus,
Chromobacterium, Neisseria, Nitrosomonas, Desulfovibrio, Geobacter,
Myxococcus, Campylobacter, Wolinella, Acinetobacter, Erwinia,
Escherichia, Legionella, Methylococcus, Pasteurella,
Photobacterium, Salmonella, Xanthomonas, Yersinia, Treponema, and
Thermotoga.
In general, "CRISPR system" refers collectively to transcripts and
other elements involved in the expression of or directing the
activity of CRISPR-associated ("Cas") genes, including sequences
encoding a Cas gene, a tracr (trans-activating CRISPR) sequence
(e.g. tracrRNA or an active partial tracrRNA), a tracr-mate
sequence (encompassing a "direct repeat" and a tracrRNA-processed
partial direct repeat in the context of an endogenous CRISPR
system), a guide sequence (also referred to as a "spacer" in the
context of an endogenous CRISPR system), or other sequences and
transcripts from a CRISPR locus. In embodiments of the invention
the terms guide sequence and guide RNA are used interchangeably. In
some embodiments, one or more elements of a CRISPR system is
derived from a type I, type II, or type III CRISPR system. In some
embodiments, one or more elements of a CRISPR system is derived
from a particular organism comprising an endogenous CRISPR system,
such as Streptococcus pyogenes. In general, a CRISPR system is
characterized by elements that promote the formation of a CRISPR
complex at the site of a target sequence (also referred to as a
protospacer in the context of an endogenous CRISPR system). In the
context of formation of a CRISPR complex, "target sequence" refers
to a sequence to which a guide sequence is designed to have
complementarity, where hybridization between a target sequence and
a guide sequence promotes the formation of a CRISPR complex. A
target sequence may comprise any polynucleotide, such as DNA or RNA
polynucleotides. In some embodiments, a target sequence is located
in the nucleus or cytoplasm of a cell.
In preferred embodiments of the invention, the CRISPR system is a
type II CRISPR system and the Cas enzyme is Cas9, which catalyzes
DNA cleavage. Enzymatic action by Cas9 derived from Streptococcus
pyogenes or any closely related Cas9 generates double stranded
breaks at target site sequences which hybridize to 20 nucleotides
of the guide sequence and that have a protospacer-adjacent motif
(PAM) sequence NGG following the 20 nucleotides of the target
sequence. CRISPR activity through Cas9 for site-specific DNA
recognition and cleavage is defined by the guide sequence, the
tracr sequence that hybridizes in part to the guide sequence and
the PAM sequence. More aspects of the CRISPR system are described
in Karginov and Hannon, The CRISPR system: small RNA-guided defense
in bacteria and archae, Mole Cell 2010, Jan. 15; 37(1): 7.
The type II CRISPR locus from Streptococcus pyogenes SF370, which
contains a cluster of four genes Cas9, Cas1, Cas2, and Csn1, as
well as two non-coding RNA elements, tracrRNA and a characteristic
array of repetitive sequences (direct repeats) interspaced by short
stretches of non-repetitive sequences (spacers, about 30 bp each).
In this system, targeted DNA double-strand break (DSB) is generated
in four sequential steps. First, two non-coding RNAs, the pre-crRNA
array and tracrRNA, are transcribed from the CRISPR locus. Second,
tracrRNA hybridizes to the direct repeats of pre-crRNA, which is
then processed into mature crRNAs containing individual spacer
sequences. Third, the mature crRNA:tracrRNA complex directs Cas9 to
the DNA target consisting of the protospacer and the corresponding
PAM via heteroduplex formation between the spacer region of the
crRNA and the protospacer DNA. Finally, Cas9 mediates cleavage of
target DNA upstream of PAM to create a DSB within the protospacer.
Several aspects of the CRISPR system can be further improved to
increase the efficiency and versatility of CRISPR targeting.
Optimal Cas9 activity may depend on the availability of free Mg2+
at levels higher than that present in the mammalian nucleus (see
e.g. Jinek et al., 2012, Science, 337:816), and the preference for
an NGG motif immediately downstream of the protospacer restricts
the ability to target on average every 12-bp in the human
genome.
Typically, in the context of an endogenous CRISPR system, formation
of a CRISPR complex (comprising a guide sequence hybridized to a
target sequence and complexed with one or more Cas proteins)
results in cleavage of one or both strands in or near (e.g. within
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50, or more base pairs from) the
target sequence. Without wishing to be bound by theory, the tracr
sequence, which may comprise or consist of all or a portion of a
wild-type tracr sequence (e.g. about or more than about 20, 26, 32,
45, 48, 54, 63, 67, 85, or more nucleotides of a wild-type tracr
sequence), may also form part of a CRISPR complex, such as by
hybridization along at least a portion of the tracr sequence to all
or a portion of a tracr mate sequence that is operably linked to
the guide sequence. In some embodiments, one or more vectors
driving expression of one or more elements of a CRISPR system are
introduced into a host cell such that expression of the elements of
the CRISPR system direct formation of a CRISPR complex at one or
more target sites. For example, a Cas enzyme, a guide sequence
linked to a tracr-mate sequence, and a tracr sequence could each be
operably linked to separate regulatory elements on separate
vectors. Alternatively, two or more of the elements expressed from
the same or different regulatory elements, may be combined in a
single vector, with one or more additional vectors providing any
components of the CRISPR system not included in the first vector.
CRISPR system elements that are combined in a single vector may be
arranged in any suitable orientation, such as one element located
5' with respect to ("upstream" of) or 3' with respect to
("downstream" of) a second element. The coding sequence of one
element may be located on the same or opposite strand of the coding
sequence of a second element, and oriented in the same or opposite
direction. In some embodiments, a single promoter drives expression
of a transcript encoding a CRISPR enzyme and one or more of the
guide sequence, tracr mate sequence (optionally operably linked to
the guide sequence), and a tracr sequence embedded within one or
more intron sequences (e.g. each in a different intron, two or more
in at least one intron, or all in a single intron). In some
embodiments, the CRISPR enzyme, guide sequence, tracr mate
sequence, and tracr sequence are operably linked to and expressed
from the same promoter.
In some embodiments, a vector comprises one or more insertion
sites, such as a restriction endonuclease recognition sequence
(also referred to as a "cloning site"). In some embodiments, one or
more insertion sites (e.g. about or more than about 1, 2, 3, 4, 5,
6, 7, 8, 9, 10, or more insertion sites) are located upstream
and/or downstream of one or more sequence elements of one or more
vectors. In some embodiments, a vector comprises an insertion site
upstream of a tracr mate sequence, and optionally downstream of a
regulatory element operably linked to the tracr mate sequence, such
that following insertion of a guide sequence into the insertion
site and upon expression the guide sequence directs
sequence-specific binding of a CRISPR complex to a target sequence
in a eukaryotic cell. In some embodiments, a vector comprises two
or more insertion sites, each insertion site being located between
two tracr mate sequences so as to allow insertion of a guide
sequence at each site. In such an arrangement, the two or more
guide sequences may comprise two or more copies of a single guide
sequence, two or more different guide sequences, or combinations of
these. When multiple different guide sequences are used, a single
expression construct may be used to target CRISPR activity to
multiple different, corresponding target sequences within a cell.
For example, a single vector may comprise about or more than about
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, or more guide sequences. In
some embodiments, about or more than about 1, 2, 3, 4, 5, 6, 7, 8,
9, 10, or more such guide-sequence-containing vectors may be
provided, and optionally delivered to a cell.
In some embodiments, a vector comprises a regulatory element
operably linked to an enzyme-coding sequence encoding a CRISPR
enzyme, such as a Cas protein. Non-limiting examples of Cas
proteins include Cas1, Cas1B, Cas2, Cas3, Cas4, Cas5, Cas6, Cas7,
Cas8, Cas9 (also known as Csn1 and Csx12), Cas10, Csy1, Csy2, Csy3,
Cse1, Cse2, Csc1, Csc2, Csa5, Csn2, Csm2, Csm3, Csm4, Csm5, Csm6,
Cmr1, Cmr3, Cmr4, Cmr5, Cmr6, Csb1, Csb2, Csb3, Csx17, Csx14,
Csx10, Csx16, CsaX, Csx3, Csx1, Csx15, Csf1, Csf2, Csf3, Csf4,
homologues thereof, or modified versions thereof. In some
embodiments, the unmodified CRISPR enzyme has DNA cleavage
activity, such as Cas9. In some embodiments, the CRISPR enzyme
directs cleavage of one or both strands at the location of a target
sequence, such as within the target sequence and/or within the
complement of the target sequence. In some embodiments, the CRISPR
enzyme directs cleavage of one or both strands within about 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 50, 100, 200, 500, or more
base pairs from the first or last nucleotide of a target sequence.
In some embodiments, a vector encodes a CRISPR enzyme that is
mutated to with respect to a corresponding wild-type enzyme such
that the mutated CRISPR enzyme lacks the ability to cleave one or
both strands of a target polynucleotide containing a target
sequence. For example, an aspartate-to-alanine substitution (D10A)
in the RuvC I catalytic domain of Cas9 from S. pyogenes converts
Cas9 from a nuclease that cleaves both strands to a nickase
(cleaves a single strand). Other examples of mutations that render
Cas9 a nickase include, without limitation, H840A, N854A, and
N863A. As a further example, two or more catalytic domains of Cas9
(RuvC I, RuvC II, and RuvC III or the HNH domain) may be mutated to
produce a mutated Cas9 substantially lacking all DNA cleavage
activity. In some embodiments, a D10A mutation is combined with one
or more of H840A, N854A, or N863A mutations to produce a Cas9
enzyme substantially lacking all DNA cleavage activity. In some
embodiments, a CRISPR enzyme is considered to substantially lack
all DNA cleavage activity when the DNA cleavage activity of the
mutated enzyme is less than about 25%, 10%, 5%, 1%, 0.1%, 0.01%, or
lower with respect to its non-mutated form. An aspartate-to-alanine
substitution (D10A) in the RuvC I catalytic domain of SpCas9
converts the nuclease into a nickase (see e.g. Sapranauskas et al.,
2011, Nucleic Acis Research, 39: 9275; Gasiunas et al., 2012, Proc.
Natl. Acad. Sci. USA, 109:E2579), such that nicked genomic DNA
undergoes the high-fidelity homology-directed repair (HDR). In some
embodiments, an enzyme coding sequence encoding a CRISPR enzyme is
codon optimized for expression in particular cells, such as
eukaryotic cells. The eukaryotic cells may be those of or derived
from a particular organism, such as a mammal, including but not
limited to human, mouse, rat, rabbit, dog, or non-human primate. In
general, codon optimization refers to a process of modifying a
nucleic acid sequence for enhanced expression in the host cells of
interest by replacing at least one codon (e.g. about or more than
about 1, 2, 3, 4, 5, 10, 15, 20, 25, 50, or more codons) of the
native sequence with codons that are more frequently or most
frequently used in the genes of that host cell while maintaining
the native amino acid sequence. Various species exhibit particular
bias for certain codons of a particular amino acid. Codon bias
(differences in codon usage between organisms) often correlates
with the efficiency of translation of messenger RNA (mRNA), which
is in turn believed to be dependent on, among other things, the
properties of the codons being translated and the availability of
particular transfer RNA (tRNA) molecules. The predominance of
selected tRNAs in a cell is generally a reflection of the codons
used most frequently in peptide synthesis. Accordingly, genes can
be tailored for optimal gene expression in a given organism based
on codon optimization. Codon usage tables are readily available,
See Nakamura. Y., et al. "Codon usage tabulated from the
international DNA sequence databases: status for the year 2000"
Nucl. Acids Res. 28:292 (2000). Computer algorithms for codon
optimizing a particular sequence for expression in a particular
host cell are also available, such as Gene Forge (Aptagen; Jacobus,
Pa.), are also available. In some embodiments, one or more codons
(e.g. 1, 2, 3, 4, 5, 10, 15, 20, 25, 50, or more, or all codons) in
a sequence encoding a CRISPR enzyme correspond to the most
frequently used codon for a particular amino acid.
In some embodiments, a vector encodes a CRISPR enzyme comprising
one or more nuclear localization sequences (NLSs), such as about or
more than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more NLSs. In
some embodiments, the CRISPR enzyme comprises about or more than
about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more NLSs at or near the
amino-terminus, about or more than about 1, 2, 3, 4, 5, 6, 7, 8, 9,
10, or more NLSs at or near the carboxy-terminus, or a combination
of these (e.g. one or more NLS at the amino-terminus and one or
more NLS at the carboxy terminus). When more than one NLS is
present, each may be selected independently of the others, such
that a single NLS may be present in more than one copy and/or in
combination with one or more other NLSs present in one or more
copies. In a preferred embodiment of the invention, the CRISPR
enzyme comprises at most 6 NLSs. In some embodiments, an NLS is
considered near the N- or C-terminus when the nearest amino acid of
the NLS is within about 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 40, 50,
or more amino acids along the polypeptide chain from the N- or
C-terminus. Non-limiting examples of NLSs include an NLS sequence
derived from: the NLS of the SV40 virus large T-antigen, having the
amino acid sequence PKKKRKV; the NLS from nucleoplasmin (e.g. the
nucleoplasmin bipartite NLS with the sequence KRPAATKKAGQAKKKK);
the c-myc NLS having the amino acid sequence PAAKRVKLD or
RQRRNELKRSP; the hRNPA1 M9 NLS having the sequence
NQSSNFGPMKGGNFGGRSSGPYGGGGQYFAKPRNQGGY; the sequence
RMRIZFKNKGKDTAELRRRRVEVSVELRKAKKDEQILKRRNV of the IBB domain from
importin-alpha; the sequences VSRKRPRP and PPKKARED of the myoma T
protein; the sequence POPKKKPL of human p53; the sequence
SALIKKKKKMAP of mouse c-abl IV; the sequences DRLRR and PKQKKRK of
the influenza virus NS1; the sequence RKLKKKIKKL of the Hepatitis
virus delta antigen; the sequence REKKKFLKRR of the mouse Mx1
protein; the sequence KRKGDEVDGVDEVAKKKSKK of the human
poly(ADP-ribose) polymerase; and the sequence RKCLQAGMNLEARKTKK of
the steroid hormone receptors (human) glucocorticoid.
In general, the one or more NLSs are of sufficient strength to
drive accumulation of the CRISPR enzyme in a detectable amount in
the nucleus of a eukaryotic cell. In general, strength of nuclear
localization activity may derive from the number of nuclear
localization sequence(s) (NLS(s)) in the CRISPR enzyme, the
particular NLS(s) used, or a combination of these factors.
Detection of accumulation in the nucleus may be performed by any
suitable technique. For example, a detectable marker may be fused
to the CRISPR enzyme, such that location within a cell may be
visualized, such as in combination with a means for detecting the
location of the nucleus (e.g. a stain specific for the nucleus such
as DAPI). Cell nuclei may also be isolated from cells, the contents
of which may then be analyzed by any suitable process for detecting
protein, such as immunohistochemistry, Western blot, or enzyme
activity assay. Accumulation in the nucleus may also be determined
indirectly, such as by an assay for the effect of CRISPR complex
formation (e.g. assay for DNA cleavage or mutation at the target
sequence, or assay for altered gene expression activity affected by
CRISPR complex formation and/or CRISPR enzyme activity), as
compared to a control no exposed to the CRISPR enzyme or complex,
or exposed to a CRISPR enzyme lacking the one or more NLSs.
In general, a guide sequence is any polynucleotide sequence having
sufficient complementarity with a target polynucleotide sequence to
hybridize with the target sequence and direct sequence-specific
binding of a CRISPR complex to the target sequence. Throughout this
application the guide sequence may be interchangeably referred to
as a guide or a spacer. In some embodiments, the degree of
complementarity between a guide sequence and its corresponding
target sequence, when optimally aligned using a suitable alignment
algorithm, is about or more than about 50%, 60%, 75%, 80%, 85%,
90%, 95%, 97.5%, 99%, or more. Optimal alignment may be determined
with the use of any suitable algorithm for aligning sequences,
non-limiting example of which include the Smith-Waterman algorithm,
the Needleman-Wunsch algorithm, algorithms based on the
Burrows-Wheeler Transform (e.g. the Burrows Wheeler Aligner),
ClustalW, Clustal X, BLAT, Novoalign (Novocraft Technologies;
available at www.novocraft.com), ELAND (Illumina, San Diego,
Calif.), SOAP (available at soap.genomics.org.cn), and Maq
(available at maq.sourceforge.net). In some embodiments, a guide
sequence is about or more than about 5, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45,
50, 75, or more nucleotides in length. In some embodiments, a guide
sequence is less than about 75, 50, 45, 40, 35, 30, 25, 20, 15, 12,
or fewer nucleotides in length. The ability of a guide sequence to
direct sequence-specific binding of a CRISPR complex to a target
sequence may be assessed by any suitable assay. For example, the
components of a CRISPR system sufficient to form a CRISPR complex,
including the guide sequence to be tested, may be provided to a
host cell having the corresponding target sequence, such as by
transfection with vectors encoding the components of the CRISPR
sequence, followed by an assessment of preferential cleavage within
the target sequence, such as by SURVEYOR assay as described herein.
Similarly, cleavage of a target polynucleotide sequence may be
evaluated in a test tube by providing the target sequence,
components of a CRISPR complex, including the guide sequence to be
tested and a control guide sequence different from the test guide
sequence, and comparing binding or rate of cleavage at the target
sequence between the test and control guide sequence reactions.
Other assays are possible, and will occur to those skilled in the
art.
A guide sequence may be selected to target any target sequence. In
some embodiments, the target sequence is a sequence within a genome
of a cell. Exemplary target sequences include those that are unique
in the target genome. For example, for the S. pyogenes Cas9, a
unique target sequence in a genome may include a Cas9 target site
of the form MMMMMMMMNNNNNNNNNNNNXGG where NNNNNNNNNNNNXGG (N is A,
G, T, or C; and X can be anything) has a single occurrence in the
genome. A unique target sequence in a genome may include an S.
pyogenes Cas9 target site of the form MMMMMMMMMNNNNNNNNNNNXGG where
NNNNNNNNNNNXGG (N is A, G, T, or C; and X can be anything) has a
single occurrence in the genome. For the S. thermophilus CRISPR1
Cas9, a unique target sequence in a genome may include a Cas9
target site of the form MMMMMMMMNNNNNNNNNNNNXXAGAAW where
NNNNNNNNNNNNXXAGAAW (N is A, G, T, or C; X can be anything; and W
is A or T) has a single occurrence in the genome. A unique target
sequence in a genome may include an S. thermophilus CRISPR1 Cas9
target site of the form MMMMMMMMMNNNNNNNNNNNXXAGAAW where
NNNNNNNNNNNXXAGAAW (N is A, G, T, or C; X can be anything; and W is
A or T) has a single occurrence in the genome. For the S. pyogenes
Cas9, a unique target sequence in a genome may include a Cas9
target site of the form MMMMMMMMNNNNNNNNNNNNXGGXG where
NNNNNNNNNNNNXGGXG (N is A, G, T, or C; and X can be anything) has a
single occurrence in the genome. A unique target sequence in a
genome may include an S. pyogenes Cas9 target site of the form
MMMMMMMMMNNNNNNNNNNNXGGXG where NNNNNNNNNNNXGGXG (N is A, G, T, or
C; and X can be anything) has a single occurrence in the genome. In
each of these sequences "M" may be A, G, T, or C, and need not be
considered in identifying a sequence as unique.
In some embodiments, a guide sequence is selected to reduce the
degree secondary structure within the guide sequence. In some
embodiments, about or less than about 75%, 50%, 40%, 30%, 25%, 20%,
15%, 10%, 5%, 1%, or fewer of the nucleotides of the guide sequence
participate in self-complementary base pairing when optimally
folded. Optimal folding may be determined by any suitable
polynucleotide folding algorithm. Some programs are based on
calculating the minimal Gibbs free energy. An example of one such
algorithm is mFold, as described by Zuker and Stiegler (Nucleic
Acids Res. 9 (1981), 133-148). Another example folding algorithm is
the online webserver RNAfold, developed at Institute for
Theoretical Chemistry at the University of Vienna, using the
centroid structure prediction algorithm (see e.g. A. R. Gruber et
al., 2008, Cell 106(1): 23-24; and P A Carr and G M Church, 2009,
Nature Biotechnology 27(12): 1151-62).
In general, a tracr mate sequence includes any sequence that has
sufficient complementarity with a tracr sequence to promote one or
more of: (1) excision of a guide sequence flanked by tracr mate
sequences in a cell containing the corresponding tracr sequence;
and (2) formation of a CRISPR complex at a target sequence, wherein
the CRISPR complex comprises the tracr mate sequence hybridized to
the tracr sequence. In general, degree of complementarity is with
reference to the optimal alignment of the tracr mate sequence and
tracr sequence, along the length of the shorter of the two
sequences. Optimal alignment may be determined by any suitable
alignment algorithm, and may further account for secondary
structures, such as self-complementarity within either the tracr
sequence or tracr mate sequence. In some embodiments, the degree of
complementarity between the tracr sequence and tracr mate sequence
along the length of the shorter of the two when optimally aligned
is about or more than about 25%, 30%, 40%, 50%, 60%, 70%, 80%, 90%,
95%, 97.5%, 99%, or higher. In some embodiments, the tracr sequence
is about or more than about 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 25, 30, 40, 50, or more nucleotides in length.
In some embodiments, the tracr sequence and tracr mate sequence are
contained within a single transcript, such that hybridization
between the two produces a transcript having a secondary structure,
such as a hairpin. In an embodiment of the invention, the
transcript or transcribed polynucleotide sequence has at least two
or more hairpins. In preferred embodiments, the transcript has two,
three, four or five hairpins. In a further embodiment of the
invention, the transcript has at most five hairpins. In a hairpin
structure the portion of the sequence 5' of the final "N" and
upstream of the loop corresponds to the tracr mate sequence, and
the portion of the sequence 3' of the loop corresponds to the tracr
sequence An example illustration of such a hairpin structure is
provided in the lower portion of FIG. 15B. Further non-limiting
examples of single polynucleotides comprising a guide sequence, a
tracr mate sequence, and a tracr sequence are as follows (listed 5'
to 3'), where "N" represents a base of a guide sequence, the first
block of lower case letters represent the tracr mate sequence, and
the second block of lower case letters represent the tracr
sequence, and the final poly-T sequence represents the
transcription terminator: (1)
NNNNNNNNNNNNNNNNNNNNgtttttgtactctcaagatttaGAAAtaaatcttgcagaagctacaaagataa-
ggctt catgccgaaatcaacaccctgtcattttatggcagggtgttttcgttatttaaTTTTTT;
(2)
NNNNNNNNNNNNNNgtttttgtacttcaGAAAtgcagaagctacaaagataaggcttcatgccgaaatca
acaccctgtcattttatggcagggtgttttcgttatttaaTTTTTT; (3)
NNNNNNNNNNNNNNNNNNgtttttgtactctcaGAAAtgcagaagctacaaagataaggcttcatgccgaaat-
ca acaccctgtcattttatggcagggtgtTTTTTT; (4)
NNNNNNNNNNNNNNNNNNNNgttttagagctaGAAAtagcaagttaaaataaggctagtccgttatcaacttg-
aaaa agtggcaccgagtcggtgcTTTTTT; (5)
NNNNNNNNNNNNNNNNNNNNgttttagagctaGAAATAGcaagttaaaataaggctagtccgttatcaacttg-
aa aaagtgTTTTTT; and (6)
NNNNNNNNNNNNNNNNNNNNgttttagagctagAAATAGcaagttaaaataaggctagtccgttatcaTTTTT
TTT. In some embodiments, sequences (1) to (3) are used in
combination with Cas9 from S. thermophilus CRISPR1. In some
embodiments, sequences (4) to (6) are used in combination with Cas9
from S. pyogenes. In some embodiments, the tracr sequence is a
separate transcript from a transcript comprising the tracr mate
sequence.
In some embodiments, a recombination template is also provided. A
recombination template may be a component of another vector as
described herein, contained in a separate vector, or provided as a
separate polynucleotide. In some embodiments, a recombination
template is designed to serve as a template in homologous
recombination, such as within or near a target sequence nicked or
cleaved by a CRISPR enzyme as a part of a CRISPR complex. A
template polynucleotide may be of any suitable length, such as
about or more than about 10, 15, 20, 25, 50, 75, 100, 150, 200,
500, 1000, or more nucleotides in length. In some embodiments, the
template polynucleotide is complementary to a portion of a
polynucleotide comprising the target sequence. When optimally
aligned, a template polynucleotide might overlap with one or more
nucleotides of a target sequences (e.g. about or more than about 1,
5, 10, 15, 20, or more nucleotides). In some embodiments, when a
template sequence and a polynucleotide comprising a target sequence
are optimally aligned, the nearest nucleotide of the template
polynucleotide is within about 1, 5, 10, 15, 20, 25, 50, 75, 100,
200, 300, 400, 500, 1000, 5000, 10000, or more nucleotides from the
target sequence.
In some embodiments, the CRISPR enzyme is part of a fusion protein
comprising one or more heterologous protein domains (e.g. about or
more than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more domains in
addition to the CRISPR enzyme). A CRISPR enzyme fusion protein may
comprise any additional protein sequence, and optionally a linker
sequence between any two domains. Examples of protein domains that
may be fused to a CRISPR enzyme include, without limitation,
epitope tags, reporter gene sequences, and protein domains having
one or more of the following activities: methylase activity,
demethylase activity, transcription activation activity,
transcription repression activity, transcription release factor
activity, histone modification activity, RNA cleavage activity and
nucleic acid binding activity. Non-limiting examples of epitope
tags include histidine (His) tags, V5 tags, FLAG tags, influenza
hemagglutinin (HA) tags, Myc tags, VSV-G tags, and thioredoxin
(Trx) tags. Examples of reporter genes include, but are not limited
to, glutathione-S-transferase (GST), horseradish peroxidase (HRP),
chloramphenicol acetyltransferase (CAT) beta-galactosidase,
beta-glucuronidase, luciferase, green fluorescent protein (GFP),
HcRed, DsRed, cyan fluorescent protein (CFP), yellow fluorescent
protein (YFP), and autofluorescent proteins including blue
fluorescent protein (BFP). A CRISPR enzyme may be fused to a gene
sequence encoding a protein or a fragment of a protein that bind
DNA molecules or bind other cellular molecules, including but not
limited to maltose binding protein (MBP), S-tag, Lex A DNA binding
domain (DBD) fusions, GALA DNA binding domain fusions, and herpes
simplex virus (HSV) BP16 protein fusions. Additional domains that
may form part of a fusion protein comprising a CRISPR enzyme are
described in US20110059502, incorporated herein by reference. In
Chalasani, US2011/0059502, it is stated, "In certain embodiments,
the activity of a second domain in a fusion protein can be about
25% to about 90% more specific than the activity of a second domain
not in a fusion protein. In some embodiments, a second domain can
comprise an endonuclease activity. In certain embodiments, a second
domain can comprise a type II endonuclease activity . . . type IIs
endonuclease activity (e.g., Fok I . . . )." Accordingly, a
functional domain as herein can be a FokI domain. In some
embodiments, a tagged CRISPR enzyme is used to identify the
location of a target sequence.
In some embodiments, a CRISPR enzyme may form a component of an
inducible system. The inducible nature of the system would allow
for spatiotemporal control of gene editing or gene expression using
a form of energy. The form of energy may include but is not limited
to electromagnetic radiation, sound energy, chemical energy and
thermal energy. Examples of inducible system include tetracycline
inducible promoters (Tet-On or Tet-Off), small molecule two-hybrid
transcription activations systems (FKBP, ABA, etc), or light
inducible systems (Phytochrome, LOV domains, or cryptochorome). In
one embodiment, the CRISPR enzyme may be a part of a Light
Inducible Transcriptional Effector (LITE) to direct changes in
transcriptional activity in a sequence-specific manner. The
components of a light may include a CRISPR enzyme, a
light-responsive cytochrome heterodimer (e.g. from Arabidopsis
thaliana), and a transcriptional activation/repression domain.
Further examples of inducible DNA binding proteins and methods for
their use are provided in U.S. 61/736,465 and U.S. 61/721,283,
which is hereby incorporated by reference in its entirety.
In some aspects, the invention comprehends delivering one or more
polynucleotides, such as or one or more vectors as described
herein, one or more transcripts thereof, and/or one or proteins
transcribed therefrom, to a host cell. In some aspects, the
invention comprehends cells produced by such methods, and animals
comprising or produced from such cells. In some embodiments, a
CRISPR enzyme in combination with (and optionally complexed with) a
guide sequence is delivered to a cell. Conventional viral and
non-viral based gene transfer methods can be used to introduce
nucleic acids in mammalian cells or target tissues. Such methods
can be used to administer nucleic acids encoding components of a
CRISPR system to cells in culture, or in a host organism. Non-viral
vector delivery systems include DNA plasmids, RNA (e.g. a
transcript of a vector described herein), naked nucleic acid, and
nucleic acid complexed with a delivery vehicle, such as a liposome.
Viral vector delivery systems include DNA and RNA viruses, which
have either episomal or integrated genomes after delivery to the
cell. For a review of gene therapy procedures, see Anderson,
Science 256:808-813 (1992); Nabel & Felgner, TIBTECH 11:211-217
(1993); Mitani & Caskey, TIBTECH 11:162-166 (1993); Dillon,
TIBTECH 11:167-175 (1993); Miller, Nature 357:455-460 (1992); Van
Brunt, Biotechnology 6(10):1149-1154 (1988); Vigne, Restorative
Neurology and Neuroscience 8:35-36 (1995); Kremer &
Perricaudet, British Medical Bulletin 51(1):31-44 (1995); Haddada
et al., in Current Topics in Microbiology and Immunology Doerfler
and Bohm (eds) (1995); and Yu et al., Gene Therapy 1:13-26
(1994).
In some embodiments, a host cell contains the target sequence, and
the cell can be derived from cells taken from a subject, such as a
cell line. A wide variety of cell lines for tissue culture are
known in the art. Examples of cell lines include, but are not
limited to, C8161, CCRF-CEM, MOLT, mIMCD-3, NHDF, HeLa-S3, Huh1,
Huh4, Huh7, HUVEC, HASMC, HEKn, HEKa, MiaPaCell, Panc1, PC-3, TF1,
CTLL-2, C1R, Rat6, CV1, RPTE, A10, T24, J82, A375, ARH-77, Calu1,
SW480, SW620, SKOV3, SK-UT, CaCo2, P388D1, SEM-K2, WEHI-231, HB56,
TIB55, Jurkat, J45.01, LRMB, Bcl-1, BC-3, IC21, DLD2, Raw264.7,
NRK, NRK-52E, MRC5, MEF, Hep G2, HeLa B, HeLa T4, COS, COS-1,
COS-6, COS-M6A, BS-C-1 monkey kidney epithelial, BALB/3T3 mouse
embryo fibroblast, 3T3 Swiss, 3T3-L1, 132-d5 human fetal
fibroblasts; 10.1 mouse fibroblasts, 293-T, 3T3, 721, 9L, A2780,
A2780ADR, A2780cis, A172, A20, A253, A431, A-549, ALC, B16, B35,
BCP-1 cells, BEAS-2B, bEnd.3, BHK-21, BR 293, BxPC3, C3H-10T1/2,
C6/36, Cal-27, CHO, CHO-7, CHO-IR, CHO-K1, CHO-K2, CHO-T, CHO
Dhfr-/-, COR-L23, COR-L23/CPR, COR-L23/5010, COR-L23/R23, COS-7,
COV-434, CML T1, CMT, CT26, D17, DH82, DU145, DuCaP, EL4, EM2, EM3,
EMT6/AR1, EMT6/AR10.0, FM3, H1299, H69, HB54, HB55, HCA2, HEK-293,
HeLa, Hepa1c1c7, HL-60, HMEC, HT-29, Jurkat, JY cells, K562 cells,
Ku812, KCL22, KG1, KYO1, LNCap, Ma-Mel 1-48, MC-38, MCF-7, MCF-10A,
MDA-MB-231, MDA-MB-468, MDA-MB-435, MDCK II, MDCK II, MOR/0.2R,
MONO-MAC 6, MTD-1A, MyEnd, NCI-H69/CPR, NCI-H69/LX10, NCI-H69/LX20,
NCI-H69/LX4, NIH-3T3, NALM-1, NW-145, OPCN/OPCT cell lines, Peer,
PNT-1A/PNT 2, RenCa, RIN-5F, RMA/RMAS, Saos-2 cells, Sf-9, SkBr3,
T2, T-47D, T84, THP1 cell line, U373, U87, U937, VCaP, Vero cells,
WM39, WT-49, X63, YAC-1, YAR, and transgenic varieties thereof.
Cell lines are available from a variety of sources known to those
with skill in the art (see, e.g., the American Type Culture
Collection (ATCC) (Manassas, Va.)). In some embodiments, a cell
transfected with one or more vectors described herein is used to
establish a new cell line comprising one or more vector-derived
sequences. In some embodiments, a cell transiently transfected with
the components of a CRISPR system as described herein (such as by
transient transfection of one or more vectors, or transfection with
RNA), and modified through the activity of a CRISPR complex, is
used to establish a new cell line comprising cells containing the
modification but lacking any other exogenous sequence. In some
embodiments, cells transiently or non-transiently transfected with
one or more vectors described herein, or cell lines derived from
such cells are used in assessing one or more test compounds. Target
sequence(s) can be in such cells.
With recent advances in crop genomics, the ability to use
CRISPR-Cas9 systems to perform efficient and cost effective gene
editing and manipulation will allow the rapid selection and
comparison of single and multiplexed genetic manipulations to
transform such genomes for improved production and enhanced traits.
In this regard reference is made to US patents and publications:
U.S. Pat. No. 6,603,061--Agrobacterium-Mediated Plant
Transformation Method; U.S. Pat. No. 7,868,149--Plant Genome
Sequences and Uses Thereof and US 2009/0100536--Transgenic Plants
with Enhanced Agronomic Traits, all the contents and disclosure of
each of which are herein incorporated by reference in their
entirety. In the practice of the invention, the contents and
disclosure of Morrell et al "Crop genomics: advances and
applications" Nat Rev Genet. 2011 Dec. 29; 13(2):85-96 are also
herein incorporated by reference in their entirety. In an
advantageous embodiment of the invention, the CRISPR/Cas9 system is
used to engineer microalgae. Thus, target polynucleotides in the
invention can be plant, algae, prokaryotic or eukaryotic.
CRISPR systems can be useful for creating an animal or cell that
may be used as a disease model. Thus, identification of target
sequences for CRISPR systems can be useful for creating an animal
or cell that may be used as a disease model. As used herein,
"disease" refers to a disease, disorder, or indication in a
subject. For example, a method of the invention may be used to
create an animal or cell that comprises a modification in one or
more nucleic acid sequences associated with a disease, or an animal
or cell in which the expression of one or more nucleic acid
sequences associated with a disease are altered. Such a nucleic
acid sequence may encode a disease associated protein sequence or
may be a disease associated control sequence.
In some methods, the disease model can be used to study the effects
of mutations on the animal or cell and development and/or
progression of the disease using measures commonly used in the
study of the disease. Alternatively, such a disease model is useful
for studying the effect of a pharmaceutically active compound on
the disease.
In some methods, the disease model can be used to assess the
efficacy of a potential gene therapy strategy. That is, a
disease-associated gene or polynucleotide can be modified such that
the disease development and/or progression is inhibited or reduced.
In particular, the method comprises modifying a disease-associated
gene or polynucleotide such that an altered protein is produced
and, as a result, the animal or cell has an altered response.
Accordingly, in some methods, a genetically modified animal may be
compared with an animal predisposed to development of the disease
such that the effect of the gene therapy event may be assessed.
CRISPR systems can be used to develop a biologically active agent
that modulates a cell signaling event associated with a disease
gene; and hence, identifying target sequences can be so used.
CRISPR systems can be used to develop a cell model or animal model
can be constructed in combination with the method of the invention
for screening a cellular function change; and hence, identifying
target sequences can be so used. Such a model may be used to study
the effects of a genome sequence modified by the CRISPR complex of
the invention on a cellular function of interest. For example, a
cellular function model may be used to study the effect of a
modified genome sequence on intracellular signaling or
extracellular signaling. Alternatively, a cellular function model
may be used to study the effects of a modified genome sequence on
sensory perception. In some such models, one or more genome
sequences associated with a signaling biochemical pathway in the
model are modified.
An altered expression of one or more genome sequences associated
with a signaling biochemical pathway can be determined by assaying
for a difference in the mRNA levels of the corresponding genes
between the test model cell and a control cell, when they are
contacted with a candidate agent. Alternatively, the differential
expression of the sequences associated with a signaling biochemical
pathway is determined by detecting a difference in the level of the
encoded polypeptide or gene product. To assay for an agent-induced
alteration in the level of mRNA transcripts or corresponding
polynucleotides, nucleic acid contained in a sample is first
extracted according to standard methods in the art. For instance,
mRNA can be isolated using various lytic enzymes or chemical
solutions according to the procedures set forth in Sambrook et al.
(1989), or extracted by nucleic-acid-binding resins following the
accompanying instructions provided by the manufacturers. The mRNA
contained in the extracted nucleic acid sample is then detected by
amplification procedures or conventional hybridization assays (e.g.
Northern blot analysis) according to methods widely known in the
art or based on the methods exemplified herein.
For purpose of this invention, amplification means any method
employing a primer and a polymerase capable of replicating a target
sequence with reasonable fidelity. Amplification may be carried out
by natural or recombinant DNA polymerases such as TaqGold.TM., T7
DNA polymerase, Klenow fragment of E. coli DNA polymerase, and
reverse transcriptase. A preferred amplification method is PCR. In
particular, the isolated RNA can be subjected to a reverse
transcription assay that is coupled with a quantitative polymerase
chain reaction (RT-PCR) in order to quantify the expression level
of a sequence associated with a signaling biochemical pathway.
Detection of the gene expression level can be conducted in real
time in an amplification assay. In one aspect, the amplified
products can be directly visualized with fluorescent DNA-binding
agents including but not limited to DNA intercalators and DNA
groove binders. Because the amount of the intercalators
incorporated into the double-stranded DNA molecules is typically
proportional to the amount of the amplified DNA products, one can
conveniently determine the amount of the amplified products by
quantifying the fluorescence of the intercalated dye using
conventional optical systems in the art. DNA-binding dye suitable
for this application include SYBR green, SYBR blue, DAPI, propidium
iodine, Hoeste, SYBR gold, ethidium bromide, acridines, proflavine,
acridine orange, acriflavine, fluorcoumanin, ellipticine,
daunomycin, chloroquine, distamycin D, chromomycin, homidium,
mithramycin, ruthenium polypyridyls, anthramycin, and the like.
In another aspect, other fluorescent labels such as sequence
specific probes can be employed in the amplification reaction to
facilitate the detection and quantification of the amplified
products. Probe-based quantitative amplification relies on the
sequence-specific detection of a desired amplified product. It
utilizes fluorescent, target-specific probes (e.g., TaqMan.RTM.
probes) resulting in increased specificity and sensitivity. Methods
for performing probe-based quantitative amplification are well
established in the art and are taught in U.S. Pat. No.
5,210,015.
In yet another aspect, conventional hybridization assays using
hybridization probes that share sequence homology with sequences
associated with a signaling biochemical pathway can be performed.
Typically, probes are allowed to form stable complexes with the
sequences associated with a signaling biochemical pathway contained
within the biological sample derived from the test subject in a
hybridization reaction. It will be appreciated by one of skill in
the art that where antisense is used as the probe nucleic acid, the
target polynucleotides provided in the sample are chosen to be
complementary to sequences of the antisense nucleic acids.
Conversely, where the nucleotide probe is a sense nucleic acid, the
target polynucleotide is selected to be complementary to sequences
of the sense nucleic acid.
Hybridization can be performed under conditions of various
stringency. Suitable hybridization conditions for the practice of
the present invention are such that the recognition interaction
between the probe and sequences associated with a signaling
biochemical pathway is both sufficiently specific and sufficiently
stable. Conditions that increase the stringency of a hybridization
reaction are widely known and published in the art. See, for
example, (Sambrook, et al., (1989); Nonradioactive In Situ
Hybridization Application Manual, Boehringer Mannheim, second
edition). The hybridization assay can be formed using probes
immobilized on any solid support, including but are not limited to
nitrocellulose, glass, silicon, and a variety of gene arrays. A
preferred hybridization assay is conducted on high-density gene
chips as described in U.S. Pat. No. 5,445,934.
For a convenient detection of the probe-target complexes formed
during the hybridization assay, the nucleotide probes are
conjugated to a detectable label. Detectable labels suitable for
use in the present invention include any composition detectable by
photochemical, biochemical, spectroscopic, immunochemical,
electrical, optical or chemical means. A wide variety of
appropriate detectable labels are known in the art, which include
fluorescent or chemiluminescent labels, radioactive isotope labels,
enzymatic or other ligands. In preferred embodiments, one will
likely desire to employ a fluorescent label or an enzyme tag, such
as digoxigenin, .beta.-galactosidase, urease, alkaline phosphatase
or peroxidase, avidin/biotin complex.
The detection methods used to detect or quantify the hybridization
intensity will typically depend upon the label selected above. For
example, radiolabels may be detected using photographic film or a
phosphoimager. Fluorescent markers may be detected and quantified
using a photodetector to detect emitted light. Enzymatic labels are
typically detected by providing the enzyme with a substrate and
measuring the reaction product produced by the action of the enzyme
on the substrate; and finally colorimetric labels are detected by
simply visualizing the colored label.
An agent-induced change in expression of sequences associated with
a signaling biochemical pathway can also be determined by examining
the corresponding gene products. Determining the protein level
typically involves a) contacting the protein contained in a
biological sample with an agent that specifically bind to a protein
associated with a signaling biochemical pathway; and (b)
identifying any agent:protein complex so formed. In one aspect of
this embodiment, the agent that specifically binds a protein
associated with a signaling biochemical pathway is an antibody,
preferably a monoclonal antibody. The reaction is performed by
contacting the agent with a sample of the proteins associated with
a signaling biochemical pathway derived from the test samples under
conditions that will allow a complex to form between the agent and
the proteins associated with a signaling biochemical pathway. The
formation of the complex can be detected directly or indirectly
according to standard procedures in the art. In the direct
detection method, the agents are supplied with a detectable label
and unreacted agents may be removed from the complex; the amount of
remaining label thereby indicating the amount of complex formed.
For such method, it is preferable to select labels that remain
attached to the agents even during stringent washing conditions. It
is preferable that the label does not interfere with the binding
reaction. In the alternative, an indirect detection procedure may
use an agent that contains a label introduced either chemically or
enzymatically. A desirable label generally does not interfere with
binding or the stability of the resulting agent:polypeptide
complex. However, the label is typically designed to be accessible
to an antibody for an effective binding and hence generating a
detectable signal. A wide variety of labels suitable for detecting
protein levels are known in the art. Non-limiting examples include
radioisotopes, enzymes, colloidal metals, fluorescent compounds,
bioluminescent compounds, and chemiluminescent compounds.
The amount of agent:polypeptide complexes formed during the binding
reaction can be quantified by standard quantitative assays. As
illustrated above, the formation of agent:polypeptide complex can
be measured directly by the amount of label remained at the site of
binding. In an alternative, the protein associated with a signaling
biochemical pathway is tested for its ability to compete with a
labeled analog for binding sites on the specific agent. In this
competitive assay, the amount of label captured is inversely
proportional to the amount of protein sequences associated with a
signaling biochemical pathway present in a test sample.
A number of techniques for protein analysis based on the general
principles outlined above are available in the art. They include
but are not limited to radioimmunoassays, ELISA (enzyme linked
immunoradiometric assays), "sandwich" immunoassays,
immunoradiometric assays, in situ immunoassays (using e.g.,
colloidal gold, enzyme or radioisotope labels), western blot
analysis, immunoprecipitation assays, immunofluorescent assays, and
SDS-PAGE.
Antibodies that specifically recognize or bind to proteins
associated with a signaling biochemical pathway are preferable for
conducting the aforementioned protein analyses. Where desired,
antibodies that recognize a specific type of post-translational
modifications (e.g., signaling biochemical pathway inducible
modifications) can be used. Post-translational modifications
include but are not limited to glycosylation, lipidation,
acetylation, and phosphorylation. These antibodies may be purchased
from commercial vendors. For example, anti-phosphotyrosine
antibodies that specifically recognize tyrosine-phosphorylated
proteins are available from a number of vendors including
Invitrogen and Perkin Elmer. Anti-phosphotyrosine antibodies are
particularly useful in detecting proteins that are differentially
phosphorylated on their tyrosine residues in response to an ER
stress. Such proteins include but are not limited to eukaryotic
translation initiation factor 2 alpha (eIF-2.alpha.).
Alternatively, these antibodies can be generated using conventional
polyclonal or monoclonal antibody technologies by immunizing a host
animal or an antibody-producing cell with a target protein that
exhibits the desired post-translational modification.
It may be desirable to discern the expression pattern of an protein
associated with a signaling biochemical pathway in different bodily
tissue, in different cell types, and/or in different subcellular
structures. These studies can be performed with the use of
tissue-specific, cell-specific or subcellular structure specific
antibodies capable of binding to protein markers that are
preferentially expressed in certain tissues, cell types, or
subcellular structures.
An altered expression of a gene associated with a signaling
biochemical pathway can also be determined by examining a change in
activity of the gene product relative to a control cell. The assay
for an agent-induced change in the activity of a protein associated
with a signaling biochemical pathway will dependent on the
biological activity and/or the signal transduction pathway that is
under investigation. For example, where the protein is a kinase, a
change in its ability to phosphorylate the downstream substrate(s)
can be determined by a variety of assays known in the art.
Representative assays include but are not limited to immunoblotting
and immunoprecipitation with antibodies such as
anti-phosphotyrosine antibodies that recognize phosphorylated
proteins. In addition, kinase activity can be detected by high
throughput chemiluminescent assays such as AlphaScreen.TM.
(available from Perkin Elmer) and eTag.TM. assay (Chan-Hui, et al.
(2003) Clinical Immunology 111: 162-174).
Where the protein associated with a signaling biochemical pathway
is part of a signaling cascade leading to a fluctuation of
intracellular pH condition, pH sensitive molecules such as
fluorescent pH dyes can be used as the reporter molecules. In
another example where the protein associated with a signaling
biochemical pathway is an ion channel, fluctuations in membrane
potential and/or intracellular ion concentration can be monitored.
A number of commercial kits and high-throughput devices are
particularly suited for a rapid and robust screening for modulators
of ion channels. Representative instruments include FLIPR.TM.
(Molecular Devices, Inc.) and VIPR (Aurora Biosciences). These
instruments are capable of detecting reactions in over 1000 sample
wells of a microplate simultaneously, and providing real-time
measurement and functional data within a second or even a
minisecond.
In practicing any of the methods disclosed herein, a suitable
vector can be introduced to a cell or an embryo via one or more
methods known in the art, including without limitation,
microinjection, electroporation, sonoporation, biolistics, calcium
phosphate-mediated transfection, cationic transfection, liposome
transfection, dendrimer transfection, heat shock transfection,
nucleofection transfection, magnetofection, lipofection,
impalefection, optical transfection, proprietary agent-enhanced
uptake of nucleic acids, and delivery via liposomes,
immunoliposomes, virosomes, or artificial virions. In some methods,
the vector is introduced into an embryo by microinjection. The
vector or vectors may be microinjected into the nucleus or the
cytoplasm of the embryo. In some methods, the vector or vectors may
be introduced into a cell by nucleofection.
The target polynucleotide of a CRISPR complex can be any
polynucleotide endogenous or exogenous to the eukaryotic cell. For
example, the target polynucleotide can be a polynucleotide residing
in the nucleus of the eukaryotic cell. The target polynucleotide
can be a sequence coding a gene product (e.g., a protein) or a
non-coding sequence (e.g., a regulatory polynucleotide or a junk
DNA).
Examples of target polynucleotides include a sequence associated
with a signaling biochemical pathway, e.g., a signaling biochemical
pathway-associated gene or polynucleotide. Examples of target
polynucleotides include a disease associated gene or
polynucleotide. A "disease-associated" gene or polynucleotide
refers to any gene or polynucleotide which is yielding
transcription or translation products at an abnormal level or in an
abnormal form in cells derived from a disease-affected tissues
compared with tissues or cells of a non disease control. It may be
a gene that becomes expressed at an abnormally high level; it may
be a gene that becomes expressed at an abnormally low level, where
the altered expression correlates with the occurrence and/or
progression of the disease. A disease-associated gene also refers
to a gene possessing mutation(s) or genetic variation that is
directly responsible or is in linkage disequilibrium with a gene(s)
that is responsible for the etiology of a disease. The transcribed
or translated products may be known or unknown, and may be at a
normal or abnormal level.
The target polynucleotide of a CRISPR complex can be any
polynucleotide endogenous or exogenous to the eukaryotic cell. For
example, the target polynucleotide can be a polynucleotide residing
in the nucleus of the eukaryotic cell. The target polynucleotide
can be a sequence coding a gene product (e.g., a protein) or a
non-coding sequence (e.g., a regulatory polynucleotide or a junk
DNA).
The target polynucleotide of a CRISPR complex may include a number
of disease-associated genes and polynucleotides as well as
signaling biochemical pathway-associated genes and polynucleotides
as listed in US provisional patent applications 61/736,527 and
61/748,427 having Broad reference BI-2011/008/WSGR Docket No.
44063-701.101 and BI-2011/008/WSGR Docket No. 44063-701.102
respectively, both entitled SYSTEMS METHODS AND COMPOSITIONS FOR
SEQUENCE MANIPULATION filed on Dec. 12, 2012 and Jan. 2, 2013,
respectively, the contents of all of which are herein incorporated
by reference in their entirety.
Examples of target polynucleotides include a sequence associated
with a signaling biochemical pathway, e.g., a signaling biochemical
pathway-associated gene or polynucleotide. Examples of target
polynucleotides include a disease associated gene or
polynucleotide. A "disease-associated" gene or polynucleotide
refers to any gene or polynucleotide which is yielding
transcription or translation products at an abnormal level or in an
abnormal form in cells derived from a disease-affected tissues
compared with tissues or cells of a non disease control. It may be
a gene that becomes expressed at an abnormally high level; it may
be a gene that becomes expressed at an abnormally low level, where
the altered expression correlates with the occurrence and/or
progression of the disease. A disease-associated gene also refers
to a gene possessing mutation(s) or genetic variation that is
directly responsible or is in linkage disequilibrium with a gene(s)
that is responsible for the etiology of a disease. The transcribed
or translated products may be known or unknown, and may be at a
normal or abnormal level.
Embodiments of the invention also relate to methods and
compositions related to knocking out genes, amplifying genes and
repairing particular mutations associated with DNA repeat
instability and neurological disorders (Robert D. Wells, Tetsuo
Ashizawa, Genetic Instabilities and Neurological Diseases, Second
Edition, Academic Press, Oct. 13, 2011--Medical). Specific aspects
of tandem repeat sequences have been found to be responsible for
more than twenty human diseases (New insights into repeat
instability: role of RNA DNA hybrids. Mclvor El, Polak U. Napierala
M. RNA Biol. 2010 September-October; 7(5):551-8). The CRISPR-Cas
system may be harnessed to correct these defects of genomic
instability. And thus, target sequences can be found in these
defects of genomic instability.
Further embodiments of the invention relate to algorithms that lay
the foundation of methods relating to CRISPR enzyme, e.g. Cas,
specificity or off-target activity. In general, algorithms refer to
an effective method expressed as a finite list of well defined
instructions for calculating one or more functions of interest.
Algorithms may be expressed in several kinds of notation, including
but not limited to programming languages, flow charts, control
tables, natural languages, mathematical formula and pseudocode. In
a preferred embodiment, the algorithm may be expressed in a
programming language that expresses the algorithm in a form that
may be executed by a computer or a computer system.
Methods relating to CRISPR enzyme, e.g. Cas, specificity or
off-target activity are based on algorithms that include but are
not limited to the thermodynamic algorithm, multiplicative
algorithm and positional algorithm. These algorithms take in an
input of a sequence of interest and identify candidate target
sequences to then provide an output of a ranking of candidate
target sequences or a score associated with a particular target
sequence based on predicted off-target sites. Candidate target
sites may be selected by an end user or a customer based on
considerations which include but are not limited to modification
efficiency, number, or location of predicted off-target cleavage.
In a more preferred embodiment, a candidate target site is unique
or has minimal predicted off-target cleavage given the previous
parameters. However, the functional relevance of potential
off-target modification should also be considered when choosing a
target site. In particular, an end user or a customer may consider
whether the off-target sites occur within loci of known genetic
function, i.e. protein-coding exons, enhancer regions, or
intergenic regulatory elements. There may also be cell-type
specific considerations, i.e. if an off-target site occurs in a
locus that is not functionally relevant in the target cell type.
Taken together, a end user or customer may then make an informed,
application-specific selection of a candidate target site with
minimal off-target modification.
The thermodynamic algorithm may be applied in selecting a CRISPR
complex for targeting and/or cleavage of a candidate target nucleic
acid sequence within a cell. The first step is to input the target
sequence (Step S400) which may have been determined using the
positional algorithm. A CRISPR complex is also input (Step S402).
The next step is to compare the target sequence with the guide
sequence for the CRISPR complex (Step S404) to identify any
mismatches. Furthermore, the amount, location and nature of the
mismatch(es) between the guide sequence of the potential CRISPR
complex and the candidate target nucleic acid sequence may be
determined. The hybridization free energy of binding between the
target sequence and the guide sequence is then calculated (Step
S406). For example, this may be calculated by determining a
contribution of each of the amount, location and nature of
mismatch(es) to the hybridization free energy of binding between
the target nucleic acid sequence and the guide sequence of
potential CRISPR complex(es). Furthermore, this may be calculated
by applying a model calculated using a training data set as
explained in more detail below. Based on the hybridization free
energy (i.e. based on the contribution analysis) a prediction of
the likelihood of cleavage at the location(s) of the mismatch(es)
of the target nucleic acid sequence by the potential CRISPR
complex(es) is generated (Step S408). The system then determines
whether or not there are any additional CRISPR complexes to
consider and if so repeats the comparing, calculating and
predicting steps. Each CRISPR complex is selected from the
potential CRISPR complex(es) based on whether the prediction
indicates that it is more likely than not that cleavage will occur
at location(s) of mismatch(es) by the CRISPR complex (Step S410).
Optionally, the probabilities of cleavage may be ranked so that a
unique CRISPR complex is selected. Determining the contribution of
each of the amount, location and nature of mismatch(es) to
hybridization free energy includes but is not limited to
determining the relative contribution of these factors. The term
"location" as used in the term "location of mismatch(es)" may refer
to the actual location of the one or more base pair mismatch(es)
but may also include the location of a stretch of base pairs that
flank the base pair mismatch(es) or a range of locations/positions.
The stretch of base pairs that flank the base pair mismatch(es) may
include but are not limited to at least one, at least two, at least
three base pairs, at least four or at least five or more base pairs
on either side of the one or more mismatch(es). As used herein, the
"hybridization free energy" may be an estimation of the free energy
of binding, e.g. DNA:RNA free energy of binding which may be
estimated from data on DNA:DNA free energy of binding and RNA:RNA
free energy of binding.
In methods relating to the multiplicative algorithm applied in
identifying one or more unique target sequences in a genome of a
eukaryotic organism, whereby the target sequence is susceptible to
being recognized by a CRISPR-Cas system, wherein the method
comprises: a) creating a data training set as to a particular Cas,
b) determining average cutting frequency at a particular position
for the particular Cas from the data training set, c) determining
average cutting frequency of a particular mismatch for the
particular Cas from the data training set, d) multiplying the
average cutting frequency at a particular position by the average
cutting frequency of a particular mismatch to obtain a first
product, e) repeating steps b) to d) to obtain second and further
products for any further particular position (s) of mismatches and
particular mismatches and multiplying those second and further
products by the first product, for an ultimate product, and
omitting this step if there is no mismatch at any position or if
there is only one particular mismatch at one particular position
(or optionally e) repeating steps b) to d) to obtain second and
further products for any further particular position (s) of
mismatches and particular mismatches and multiplying those second
and further products by the first product, for an ultimate product,
and omitting this step if there is no mismatch at any position or
if there is only one particular mismatch at one particular
position), and f) multiplying the ultimate product by the result of
dividing the minimum distance between consecutive mismatches by 18
and omitting this step if there is no mismatch at any position or
if there is only one particular mismatch at one particular position
(or optionally f) multiplying the ultimate product by the result of
dividing the minimum distance between consecutive mismatches by 18
and omitting this step if there is no mismatch at any position or
if there is only one particular mismatch at one particular
position), to thereby obtain a ranking, which allows for the
identification of one or more unique target sequences, the
predicted cutting frequencies for genome-wide targets may be
calculated by multiplying, in series:
f.sub.est=f(1)g(N.sub.1,N.sub.1').times.f(2)g(N.sub.2N.sub.2').times.
. . . f(19)g(N.sub.19,N.sub.19').times.h with values f(i) and
g(N.sub.i,N.sub.i') at position i corresponding, respectively, to
the aggregate position- and base-mismatch cutting frequencies for
positions and pairings indicated in a generalized base transition
matrix or an aggregate matrix, e.g. a matrix as indicated in FIG.
12c. Each frequency was normalized to range from 0 to 1, such that
f.fwdarw.(f-f.sub.min)/(f.sub.max-f.sub.min). In case of a match,
both were set equal to 1. The value h meanwhile re-weighted the
estimated frequency by the minimum pairwise distance between
consecutive mismatches in the target sequence. This value distance,
in base-pairs, was divided by 18 to give a maximum value of 1 (in
cases where fewer than 2 mismatches existed, or where mismatches
occurred on opposite ends of the 19 bp target-window). Samples
having a read-count of at least 10,000 (n=43) were plotted. Those
tied in rank were given a rank-average. The Spearman correlation
coefficient, 0.58, indicated that the estimated frequencies
recapitulated 58% of the rank-variance for the observed cutting
frequencies. Comparing f.sub.est with the cutting frequencies
directly yielded a Pearson correlation of 0.89. While dominated by
the highest-frequency gRNA/target pairs, this value indicated that
nearly 90% of all cutting-frequency variance was explained by the
predictions above. In further aspects of the invention, the
multiplicative algorithm or the methods mentioned herein may also
include thermodynamic factors, e.g. hybridization energies, or
other factors of interest being multiplied in series to arrive at
the ultimate product.
In embodiments of the invention, determining the off-target
activity of a CRISPR enzyme may allow an end user or a customer to
predict the best cutting sites in a genomic locus of interest. In a
further embodiment of the invention, one may obtain a ranking of
cutting frequencies at various putative off-target sites to verify
in vitro, in vivo or ex vivo if one or more of the worst case
scenario of non-specific cutting does or does not occur. In another
embodiment of the invention, the determination of off-target
activity may assist with selection of specific sites if an end user
or customer is interested in maximizing the difference between
on-target cutting frequency and the highest cutting frequency
obtained in the ranking of off-target sites. Another aspect of
selection includes reviewing the ranking of sites and identifying
the genetic loci of the non-specific targets to ensure that a
specific target site selected has the appropriate difference in
cutting frequency from say targets that may encode for oncogenes or
other genetic loci of interest. Aspects of the invention may
include methods of minimizing therapeutic risk by verifying the
off-target activity of the CRISPR-Cas complex. Further aspects of
the invention may include utilizing information on off-target
activity of the CRISPR-Cas complex to create specific model systems
(e.g. mouse) and cell lines. The methods of the invention allow for
rapid analysis of non-specific effects and may increase the
efficiency of a laboratory.
In methods relating to the positional algorithm applied in
identifying one or more unique target sequences in a genome of a
eukaryotic organism, whereby the target sequence is susceptible to
being recognized by a CRISPR-Cas system, wherein the method
comprises: a) determining average cutting frequency of
guide-RNA/target mismatches at a particular position for a
particular Cas from a training data set as to that Cas, if more
than one mismatch, repeat step a) so as to determine cutting
frequency for each mismatch, multiply frequencies of mismatches to
thereby obtain a ranking, which allows for the identification of
one or more unique target sequences, an example of an application
of this algorithm may be seen in FIG. 23.
FIGS. 32, 33A, 33B and 34, respectively, each show a flow diagram
of methods of the invention. FIG. 32 provides a flow diagram as to
locational or positional methods of the invention, i.e., with
respect to computational identification of unique CRISPR target
sites: To identify unique target sites for a Cas, e.g., a Cas9,
e.g., the S. pyogenes SF370 Cas9 (SpCas9) enzyme, in nucleic acid
molecules, e.g., of cells, e.g., of organisms, which include but
are not limited to human, mouse, rat, zebrafish, fruit fly, and C.
elegans genome, Applicants developed a software package to scan
both strands of a DNA sequence and identify all possible SpCas9
target sites. The method is shown in FIG. 32 which shows that the
first step is to input the genome sequence (Step S100). The CRISPR
motif(s) which are suitable for this genome sequence are then
selected (Step S102). For this example, the CRISPR motif is an NGG
protospacer adjacent motif (PAM) sequence. A fragment of fixed
length which needs to occur in the overall sequence before the
selected motif (i.e. upstream in the sequence) is then selected
(Step S102). In this case, the fragment is a 20 bp sequence. Thus,
each SpCas9 target site was is operationally defined as a 20 bp
sequence followed by an NGG protospacer adjacent motif (PAM)
sequence, and all sequences satisfying this 5'-N20-NGG-3'
definition on all chromosomes were identified (Step S106). To
prevent non-specific genome editing, after identifying all
potential sites, all target sites were filtered based on the number
of times they appear in the relevant reference genome (Step S108).
(Essentially, all the 20-bp fragments (candidate target sites)
upstream of the NGG PAM motif are aggregated. If a particular 20-bp
fragment occurs more than once in your genome-wide search, it is
considered not unique and `strikes out`, aka filtered. The 20-bp
fragments that REMAIN therefore occur only once in the target
genome, making it unique; and, instead of taking a 20-bp fragment
(the full Cas9 target site), this algorithm takes the first, for
example, 11-12 bp upstream of the PAM motif and requires that to be
unique.) Finally, a unique target site is selected (Step S110),
e.g. To take advantage of sequence specificity of Cas, e.g., Cas9
activity conferred by a `seed` sequence, which can be, for example,
approximately 11-12 bp sequence 5' from the PAM sequence,
5'-NNNNNNNNNN-NGG-3' sequences were selected to be unique in the
relevant genome. Genomic sequences are available on the UCSC Genome
Browser and sample visualizations of the information for the Human
genome hg, Mouse genome mm, Rat genome rn, Zebrafish genome danRer,
D. melanogaster genome dm, C. elegans genome cc, the pig genome and
cow genome are shown in FIGS. 15 through 22 respectively.
FIGS. 33A and 33B each provides a flow diagram as to thermodynamic
methods of the invention. FIG. 34 provides a flow diagram as to
multiplication methods of the invention. Referring to FIGS. 33A and
33B, and considering the least squares thermodynamic model of
CRISPR-Cas cutting efficiency, for arbitrary Cas9 target sites,
Applicants generated a numerical thermodynamic model that predicts
Cas9 cutting efficiency. Applicants propose 1) that the Cas9 guide
RNA has specific free energies of hybridization to its target and
any off-target DNA sequences and 2) that Cas9 modifies RNA:DNA
hybridization free-energies locally in a position-dependent but
sequence-independent way. Applicants trained a model for predicting
CRISPR-Cas cutting efficiency based on their CRISPR-Cas guide RNA
mutation data and RNA:DNA thermodynamic free energy calculations
using a machine learning algorithm. Applicants then validated their
resulting models by comparing their predictions of CRISPR-Cas
off-target cutting at multiple genomic loci with experimental data
assessing locus modification at the same sites. The methodology
adopted in developing this algorithm is as follows: The problem
summary states that for arbitrary spacers and targets of constant
length, a numerical model that makes thermodynamic sense and
predicts Cas9 cutting efficiency is to be found. Suppose Cas9
modifies DNA:RNA hybridization free-energies locally in a
position-dependent but sequence-independent way. The first step is
to define a model having a set a weights which links the free
energy of hybridization Z with the local free energies G (Step
S200). Then for DNA:RNA hybridization free energies
.DELTA.G.sub.ij(k) (for position k between 1 and N) of spacer i and
target j
.times..times..alpha..times..DELTA..times..times..function.
##EQU00003##
Z.sub.ij can be treated as an "effective" free-energy modified by
the multiplicative position-weights .alpha..sub.k. The "effective"
free-energy Z.sub.ij corresponds to an associated
cutting-probability .about.e.sup.-.beta.Z.sup.ij (for some constant
.beta.) in the same way that an equilibrium model of hybridization
(without position-weighting) would have predicted a
hybridization-probability .about.e.sup.-.beta..DELTA.G.sup.ij.
Since cutting-efficiency has been measured, the values Z.sub.ij can
be treated as their observables. Meanwhile, .DELTA.G.sub.ij(k) can
be calculated for any experiment's spacer-target pairing.
Applicants task was to find the values .alpha..sub.k, since this
would allow them to estimate Z.sub.ij for any spacer-target pair.
The weights are determined by inputting known values for Z and G
from a training set of sequences with the known values being
determined by experimentation as necessary. Thus, Applicants need
to define a training set of sequences (Step S202) and calculate a
value of Z for each sequence in the training set (Step S204).
Writing the above equation for Z.sub.ij in matrix form Applicants
get: {right arrow over (Z)}=G{right arrow over (.alpha.)} (1)
The least-squares estimate is then {right arrow over
(.alpha.)}.sub.est=(G.sup.TG).sub.-1G.sup.T{right arrow over
(Z)}
where G.sup.T is the matrix-transpose of the G and
(G.sup.TG).sup.-1 is the inverse of their matrix-product. In the
above G is a matrix of local DNA:RNA free-energy values whose rth
row corresponds to experimental trial r and whose kth column
corresponds to the kth position in the DNA:RNA hybrid tested in
that experimental trial. These values of G are thus input into the
training system (Step S204). {right arrow over (Z)} is meanwhile a
column-vector whose rth row corresponds to observables from the
same experimental trial as G's rth row. Because of the relation
described above wherein the CRISPR cutting frequencies are
estimated to vary as .about.e.sup.-.beta.Z.sup.ij, these
observables, Z.sub.ij, were calculated as the natural logarithm of
the observed cutting frequency. The observable is the cleavage
efficiency of Cas, e.g., Cas9, at a target DNA for a particular
guide RNA and target DNA pair. The experiment is Cas, e.g., Cas9,
with a particular sgRNA/DNA target pairing, and the observable is
the cleavage percentage (whether measured as indel formation
percentage from cells or simply cleavage percentage in vitro) (see
herein discussion on generating training data set). More in
particular, every unique PCR reaction that was sequenced should be
treated as a unique experimental trial to encompass replicability
within the vector. This means that experimental replicates each go
into separate rows of equation 1 (and because of this, some rows of
G will be identical). The advantage of this is that when {right
arrow over (.alpha.)} is fit, all relevant information--including
replicability--is taken into account in the final estimate.
Observable {right arrow over (Z)}, values were calculated as log
(observed frequency of cutting) (Step S206). Cutting frequencies
were optionally normalized identically (so that they all have the
same "units") (Step S208). For plugging in sequencing
indel-frequency values, it may be best, however, to standardize
sequencing depth. The preferred way to do this would be to set a
standard sequencing-depth D for which all experiments included in
{right arrow over (Z)} have at least that number of reads. Since
cutting frequencies below 1/D cannot be consistently detected, this
should be set as the minimum frequency for the data-set, and the
values in {right arrow over (Z)} should range from log(1/D) to
log(1). One could vary the value of D later on to ensure that the
{right arrow over (.alpha.)} estimate isn't too dependent on the
value chosen. Thus, values of Z could be filtered out if they do
not meet the minimum sequencing depth (Step S210). Once the values
of G and Z are input to the machine learning system, the weights
can be determined (Step S212) and output (Step S214). These weights
can then be used to estimate the free energy Z and the cutting
frequency for any sequence. In a further aspect, there are
different methods of graphing NGG and NNAGAAW sequences. One is
with the `non-overlapping` method. NGG and NRG may be regraphed in
an "overlapping" fashion, as indicated in FIGS. 6 A-C. Applicants
also performed a study on off target Cas9 activity as indicated in
FIGS. 10, 11 and 12. Aspects of the invention also relate to
predictive models that may not involve hybridization energies but
instead simply use the cutting frequency information as a
prediction.
FIG. 34 shows the steps in one method relating to the
multiplicative algorithm which may be applied in identifying one or
more unique target sequences in a genome of a eukaryotic organism,
whereby the target sequence is susceptible to being recognized by a
CRISPR-Cas system. The method comprises: a) creating a data
training set as to a particular Cas. The data training set may be
created as described in more detail later by determining the
weights associated with a model. Once a data training set has been
established, it can be used to predict the behavior of an input
sequence and to identify one or more unique target sequences
therein. At step S300, the genome sequence is input to the system.
For a particular Cas, the next step is to locate a mismatch between
a target sequence within the input sequence and guide RNA for the
particular Cas (Step S302). For the identified mismatch, two
average cutting frequencies are determined using the data training
set. These are the average cutting frequency at the position of the
mismatch (step S304) and the average cutting frequency associated
with that type of mismatch (Step S306). These average cutting
frequencies are determined from the data training set which is
particular to that Cas. The next step S308 is to create a product
by multiplying the average cutting frequency at a particular
position by the average cutting frequency of a particular mismatch
to obtain a first product. It is then determined at step S310
whether or not there are any other mismatches. If there are none,
the target sequence is output as the unique target sequence.
However, if there are other mismatches, steps 304 to 308 are
repeated to obtain second and further products for any further
particular position (s) of mismatches and particular mismatches.
Where second and further products are created and all products are
multiplied together to create an ultimate product. The ultimate
product is then multiplied by the result of dividing the minimum
distance between consecutive mismatches by the length of the target
sequence (e.g. 18) (step S314) which effectively scales each
ultimate product. It will be appreciated that steps 312 and 314 are
omitted if there is no mismatch at any position or if there is only
one particular mismatch at one particular position. The process is
then repeated for any other target sequences. The "scaled" ultimate
products for each target sequence are each ranked to thereby obtain
a ranking (Step S316), which allows for the identification of one
or more unique target sequences by selecting the highest ranked one
(Step S318). Thus the "scaled" ultimate product which represents
the predicted cutting frequencies for genome-wide targets may be
calculated by:
f.sub.est=f(1)g(N.sub.1,N.sub.1').times.f(2)g(N.sub.2,N.sub.2').times.
. . . f(19)g(N.sub.19,N.sub.19').times.h.
with values f(i) and g(N.sub.i, N.sub.i') at position i
corresponding, respectively, to the aggregate position- and
base-mismatch cutting frequencies for positions and pairings
indicated in a generalized base transition matrix or an aggregate
matrix, e.g. a matrix as indicated in FIG. 12c. In other words,
f(i) is the average cutting frequency at the particular position
for the mismatch and g(N.sub.i, N'.sub.i) is the average cutting
frequency for the particular mismatch type for the mismatch. Each
frequency was normalized to range from 0 to 1, such that
f.fwdarw.(f-f.sub.min)/(f.sub.max-f.sub.min). In case of a match,
both were set equal to 1. The value h meanwhile re-weighted the
estimated frequency by the minimum pairwise distance between
consecutive mismatches in the target sequence. This value distance,
in base-pairs, was divided by a constant which was indicative of
the length of the target sequence (e.g. 18) to give a maximum value
of 1 (in cases where fewer than 2 mismatches existed, or where
mismatches occurred on opposite ends of the 19 bp target-window).
Samples having a read-count of at least 10,000 (n=43) were plotted.
Those tied in rank were given a rank-average. The Spearman
correlation coefficient, 0.58, indicated that the estimated
frequencies recapitulated 58% of the rank-variance for the observed
cutting frequencies. Comparing fir with the cutting frequencies
directly yielded a Pearson correlation of 0.89. While dominated by
the highest-frequency gRNA/target pairs, this value indicated that
nearly 90% of all cutting-frequency variance was explained by the
predictions above. In further aspects of the invention, the
multiplicative algorithm or the methods mentioned herein may also
include thermodynamic factors, e.g. hybridization energies, or
other factors of interest being multiplied in series to arrive at
the ultimate product.
FIG. 35 shows a schematic block diagram of a computer system which
can be used to implement the methods described herein. The computer
system 50 comprises a processor 52 coupled to code and data memory
54 and an input/output system 56 (for example comprising interfaces
for a network and/or storage media and/or other communications).
The code and/or data stored in memory 54 may be provided on a
removable storage medium 60. There may also be a user interface 58
for example comprising a keyboard and/or mouse and a user display
62. The computer system is connected to a database 78. The database
78 comprises the data associated with the data training sets. The
computer system is shown as a single computing device with multiple
internal components which may be implemented from a single or
multiple central processing units, e.g. microprocessors. It will be
appreciated that the functionality of the device may be distributed
across several computing devices. It will also be appreciated that
the individual components may be combined into one or more
components providing the combined functionality. Moreover, any of
the modules, databases or devices shown may be implemented in a
general purpose computer modified (e.g. programmed or configured)
by software to be a special-purpose computer to perform the
functions described herein. The processor may be configured to
carry out the steps shown in the various flowcharts. The user
interface may be used to input the genome sequence, the CRISPR
motif and/or Cas for which a target sequence is to be identified.
The output unique target sequence(s) may be displayed on the user
display.
EXAMPLES
The following examples are given for the purpose of illustrating
various embodiments of the invention and are not meant to limit the
present invention in any fashion. The present examples, along with
the methods described herein are presently representative of
preferred embodiments, are exemplary, and are not intended as
limitations on the scope of the invention. Changes therein and
other uses which are encompassed within the spirit of the invention
as defined by the scope of the claims will occur to those skilled
in the art.
Example 1
Evaluation of the Specificity of Cas9-Mediated Genome Cleavage
Applicants carried out an initial test to evaluate the cleavage
specificity of Cas9 from Streptococcus pyogenes. The assay was
designed to test the effect of single basepair mismatches between
the guide RNA sequence and the target DNA. The results from the
initial round of testing are depicted in FIG. 3.
Applicants carried out the assay using 293FT cells in 96 well
plates. Cells were transfected with 65 ng of a plasmid carrying
Cas9 and 10 ng of a PCR amplicon carrying the pol3 promoter U6 and
the guide RNA. The experiment was conducted using a high amount of
Cas9 and guide RNA, which probably explains the seemingly low
specificity (i.e. single base mismatches is not sufficient to
abolish cleavage). Applicants also evaluate the effect of different
concentration of Cas9 and RNA on cleavage specificity.
Additionally, Applicants carry out a comprehensive evaluation of
every possible mismatch in each position of the guide RNA. The end
goal is to generate a model to inform the design of guide RNAs
having high cleavage specificity.
Additional experiments test position and number of mismatches in
the guide RNA on cleavage efficiency. The following table shows a
list of 48 mismatch possibilities. In the table 0 means no mutation
and 1 means with mutation.
TABLE-US-00002 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1
NGG Test Rule 1: More mismatches = bigger effect on cutting Test
Rule 2: Mismatches on 5' end have less effect than mismatches on 3'
end 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 1 1 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 4 0 0
0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0
0 0 0 0 0 6 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 1
1 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
9 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 1 1 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 12 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 13 0 0 0 0 0 0 0 0 0 0 0 0 0 1
1 1 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 15 0 0 0 0 0
0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 16 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0
0 0 0 17 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 18 0 0 0 0 0 0 1 1
1 1 1 0 0 0 0 0 0 0 0 0 19 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
20 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 21 0 1 1 1 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 22 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Test
Rule 3: Mismatches more spreadout have less effect than mismatches
more concentrated 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 24 0 0
0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 25 0 0 0 0 0 0 0 0 0 0 1 0 0 0
0 0 0 0 0 1 26 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 27 1 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 28 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
1 0 0 29 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 30 0 0 0 0 0 0 0 0
1 0 0 0 0 0 0 0 0 1 0 0 31 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
32 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 33 0 0 0 0 0 0 0 0 0 0 1
0 0 0 0 1 0 0 0 0 34 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 35 0 1
0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 36 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 1 0 1 0 1 37 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 38 0 0 0 0 0
0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 39 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0
0 0 1 40 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 41 0 0 0 0 0 0 0 0
0 0 0 1 0 0 1 0 0 1 0 0 42 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0
43 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 44 0 0 0 0 0 0 0 0 0 0 0
0 0 1 0 1 0 1 0 1 45 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 1 46 0 0
0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 47 0 0 0 0 1 0 0 0 0 1 0 0 0 0
1 0 0 0 0 1 48 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1
Example 2
Evaluation of Mutations in the PAM Sequence, and its Effect on
Cleavage Efficiency
Applicants tested mutations in the PAM sequence and its effect on
cleavage. The PAM sequence for Streptococcus pyogenes Cas9 is NGG,
where the GG is thought to be required for cleavage. To test
whether Cas9 can cleavage sequences with PAMs that are different
than NGG, Applicants chose the following 30 target sites from the
Emx1 locus of the human genome--2 for each of the 15 PAM
possibilities: NAA, NAC, NAT, NAG, NCA, NCC, NCG, NCT, NTA, NTC,
NTG, NTT, NGA, NGC, and NGT; NGG is not selected because it can be
targeted efficiently.
The cleavage efficiency data is shown in FIG. 4. The data shows
that other than NGG, only sequences with NAG PAMs can be
targeted.
TABLE-US-00003 PAM Target 1 Target 2 NAA AGGCCCCAGTGGCTGCTCT
TCATCTGTGCCCCTCCCTC NAT ACATCAACCGGTGGCGCAT GGGAGGACATCGATGTCAC NAC
AAGGTGTGGTTCCAGAACC CAAACGGCAGAAGCTGGAG NAG CCATCACATCAACCGGTGG
GGGTGGGCAACCACAAACC NTA AAACGGGAGAAGCTGGAGG GGTGGGCAACCACAAACCC NTT
GGCAGAAGCTGGAGGAGGA GGCTCCCATCACATCAACC NTC GGTGTGGTTCCAGAACCGG
GAAGGGCCTGAGTCCGAGC NTG AACCGGAGGACAAAGTACA CAACCGGTGGCGCATTGCC NCA
TTCCAGAACCGGAGGACAA AGGAGGAAGGGCCTGAGTC NCT GTGTGGTTCCAGAACCGGA
AGCTGGAGGAGGAAGGGCC NCC TCCAGAACCGGAGGACAAA GCATTGCCACGAAGCAGGC NCG
CAGAAGCTGGAGGAGGAAG ATTGCCACGAAGCAGGCCA NGA CATCAACCGGTGGCGCATT
AGAACCGGAGGACAAAGTA NGT GCAGAAGCTGGAGGAGGAA TCAACCGGTGGCGCATTGC NGC
CCTCCCTCCCTGGCCCAGG GAAGCTGGAGGAGGAAGGG
Example 3
Cas9 Diversity and RNAs, PAMS, Targets
The CRISPR-Cas system is an adaptive immune mechanism against
invading exogenous DNA employed by diverse species across bacteria
and archaea. The type II CRISPR-Cas9 system consists of a set of
genes encoding proteins responsible for the "acquisition" of
foreign DNA into the CRISPR locus, as well as a set of genes
encoding the "execution" of the DNA cleavage mechanism; these
include the DNA nuclease (Cas9), a non-coding transactivating
cr-RNA (tracrRNA), and an array of foreign DNA-derived spacers
flanked by direct repeats (crRNAs). Upon maturation by Cas9, the
tracrRNA and crRNA duplex guide the Cas9 nuclease to a target DNA
sequence specified by the spacer guide sequences, and mediates
double-stranded breaks in the DNA near a short sequence motif in
the target DNA that is required for cleavage and specific to each
CRISPR-Cas system. The type II CRISPR-Cas systems are found
throughout the bacterial kingdom (FIGS. 7 and 8A-F) and highly
diverse in Cas9 protein sequence and size, tracrRNA and crRNA
direct repeat sequence, genome organization of these elements, and
the motif requirement for target cleavage. One species may have
multiple distinct CRISPR-Cas systems.
Applicants evaluated 207 putative Cas9s from bacterial species
(FIG. 8A-F) identified based on sequence homology to known Cas9s
and structures orthologous to known subdomains. Using the method of
Example 1, Applicants will carry out a comprehensive evaluation of
every possible mismatch in each position of the guide RNA for these
different Cas9s to generate a model to inform the design of guide
RNAs having high cleavage specificity for each based on the impact
of the test position and number of mismatches in the guide RNA on
cleavage efficiency for each Cas9.
The CRISPR-Cas system is amenable for achieving tissue-specific and
temporally controlled targeted deletion of candidate disease genes.
Examples include but are not limited to genes involved in
cholesterol and fatty acid metabolism, amyloid diseases, dominant
negative diseases, latent viral infections, among other disorders.
Accordingly, target sequences can be in candidate disease genes,
e.g.:
TABLE-US-00004 Disease GENE SPACER PAM Mechanism References
Hypercholes- HMG- GCCAAATTG CGG Knockout Fluvastatin: a review of
its terolemia CR GACGACCCT pharmacology and use in the CG
management of hypercholesterolaemia. (Plosker GL et al. Drugs 1996,
51(3):433- 459) Hypercholes- SQLE CGAGGAGAC TGG Knockout Potential
role of nonstatin terolemia CCCCGTTTC cholesterol lowering agents
GG (Trapani et al. IUBMB Life, Volume 63, Issue 11, pages 964- 971,
November 2011) Hyper- DGAT CCCGCCGCC AGG Knockout DGAT1 inhibitors
as anti-obesity lipidemia 1 GCCGTGGCT and anti-diabetic agents.
(Birch CG AM et al. Current Opinion in Drug Discovery &
Development [2010, 13(4):489-496) Leukemia BCR- TGAGCTCTA AGG
Knockout Killing of leukemic cells with a ABL CGAGATCCA BCR/ABL
fusion gene by RNA CA interference (RNAi). (Fuchs et al. Oncogene
2002, 21(37):5716- 5724)
Examples of a pair of guide-RNA to introduce chromosomal
microdeletion at a gene locus
TABLE-US-00005 Disease GENE SPACER PAM Mechanism References
Hyperlip- PLIN2 CTCAAAATT TGG Micro- Perilipin-2 Null Mice are
idemia guide1 CATACCGGT deletion Protected Against Diet-Induced TG
Obesity, Adipose Inflammation and Fatty Liver Disease (McManaman JL
et al. The Journal of Lipid Research, jlr.M035063. First Published
on February 12, 2013) Hyperlip- PLIN2 CGTTAAACA TGG Micro- idemia
guide2 ACAACCGGA deletion CT Hyperlip- SREBP TTCACCCCG ggg Micro-
Inhibition of SREBP by a Small idemia guide1 CGGCGCTGA deletion
Molecule, Betulin, Improves AT Hyperlipidemia and Insulin
Resistance and Reduces Atherosclerotic Plaques (Tang J et al. Cell
Metabolism, Volume 13, Issue 1, 44-56, 5 January 2011) Hyperlip-
SREBP ACCACTACC agg Micro- idemia guide2 AGTCCGTCC deletion AC
Examples of potential HIV-1 targeted spacers adapted from Mcintyre
et al, which generated shRNAs against HIV-1 optimized for maximal
coverage of HIV-1 variants. CACTGCTTAAGCCTCGCTCGAGG
TCACCAGCAATATTCGCTCGAGG CACCAGCAATATTCCGCTCGAGG
TAGCAACAGACATACGCTCGAGG GGGCAGTAGTAATACGCTCGAGG
CCAATTCCCATACATTATTGTAC
Identification of Cas9 target site: Applicants analyzed the human
CFTR genomic locus and identified the Cas9 target site (PAM may
contain a NGG or a NNAGAAW motif). The frequency of these PAM
sequences in the human genome are shown in FIG. 5.
Protospacer IDs and their corresponding genomic target, protospacer
sequence, PAM sequence, and strand location are provided in the
below Table. Guide sequences were designed to be complementary to
the entire protospacer sequence in the case of separate transcripts
in the hybrid system, or only to the underlined portion in the case
of chimeric RNAs.
TABLE-US-00006 TABLE Protospacer IDs and their corresponding
genomic target, protospacer sequence, PAM sequence, and strand
location proto- protospacer spacer genomic sequence ID target (5'
to 3') PAM strand 1 EMX1 GGACATCGATGTCAC TGG + CTCCAATGACTAGGG 2
EMX1 CATTGGAGGTGACAT TGG - CGATGTCCTCCCCAT 3 EMX1 GGAAGGGCCTGAGTC
GGG + CGAGCAGAAGAAGAA 4 PVALB GGTGGCGAGAGGGGC AGG + CGAGATTGGGTGTTC
5 PVALB ATGCAGGAGGGTGGC TGG + GAGAGGGGCCGAGAT
Computational Identification of Unique CRISPR Target Sites:
To identify unique target sites for a Cas, e.g., a Cas9, e.g., the
S. pyogenes SF370 Cas9 (SpCas9) enzyme, in nucleic acid molecules,
e.g., of cells, e.g., of organisms, which include but are not
limited to human, mouse, rat, zebrafish, fruit fly, and C. elegans
genome, Applicants developed a software package to scan both
strands of a DNA sequence and identify all possible SpCas9 target
sites. For this example, each SpCas9 target site was operationally
defined as a 20 bp sequence followed by an NGG protospacer adjacent
motif (PAM) sequence, and all sequences satisfying this
5'-N.sub.20-NGG-3' definition on all chromosomes were identified.
To prevent non-specific genome editing, after identifying all
potential sites, all target sites were filtered based on the number
of times they appear in the relevant reference genome. To take
advantage of sequence specificity of Cas, e.g., Cas9 activity
conferred by a `seed` sequence, which can be, for example,
approximately 11-12 bp sequence 5' from the PAM sequence,
5'-NNNNNNNNNN-NGG-3' sequences were selected to be unique in the
relevant genome. Genomic sequences are available on the UCSC Genome
Browser and sample visualizations of the information for the Human
genome hg, Mouse genome mm, Rat genome rn, Zebrafish genome danRer,
D. melanogaster genome dm, C. elegans genome ce, the pig genome and
cow genome are shown in FIGS. 15 through 22 respectively.
A similar analysis may be carried out for other Cas enzymes
utilizing their respective PAM sequences, for e.g. Staphylococcus
aureus sp. Aureus Cas9 and its PAM sequence NNGRR (FIG. 31).
Example 4
Experimental Architecture for Evaluating CRISPR-Cas Target Activity
and Specificity
Targeted nucleases such as the CRISPR-Cas systems for gene editing
applications allow for highly precise modification of the genome.
However, the specificity of gene editing tools is a crucial
consideration for avoiding adverse off-target activity. Here,
Applicants describe a Cas9 guide RNA selection algorithm that
predicts off-target sites for any desired target site within
mammalian genomes.
Applicants constructed large oligo libraries of guide RNAs carrying
combinations of mutations to study the sequence dependence of Cas9
programming. Using next-generation deep sequencing, Applicants
studied the ability of single mutations and multiple combinations
of mismatches within different Cas9 guide RNAs to mediate target
DNA locus modification. Applicants evaluated candidate off-target
sites with sequence homology to the target site of interest to
assess any off-target cleavage.
Algorithm for Predicting CRISPR-Cas Target Activity and
Specificity:
Data from these studies were used to develop algorithms for the
prediction of CRISPR-Cas off-target activity across the human
genome. The Applicants' resulting computational platform supports
the prediction of all CRISPR-Cas system target activity and
specificity in any genome. Applicants evaluate CRISPR-Cas activity
and specificity by predicting the Cas9 cutting efficiency for any
CRISPR-Cas target against all other genomic CRISPR-Cas targets,
excluding constraining factors, i.e., some epigenetic modifications
like repressive chromatin/heterochromatin.
The algorithms Applicants describe 1) evaluate any target site and
give potential off-targets and 2) generate candidate target sites
for any locus of interest with minimal predicted off-target
activity.
Least Squares Thermodynamic Model of CRISPR-Cas Cutting
Efficiency:
For arbitrary Cas9 target sites, Applicants generated a numerical
thermodynamic model that predicts Cas9 cutting efficiency.
Applicants propose 1) that the Cas9 guide RNA has specific free
energies of hybridization to its target and any off-target DNA
sequences and 2) that Cas9 modifies RNA:DNA hybridization
free-energies locally in a position-dependent but
sequence-independent way. Applicants trained a model for predicting
CRISPR-Cas cutting efficiency based on their CRISPR-Cas guide RNA
mutation data and RNA:DNA thermodynamic free energy calculations
using a machine learning algorithm. Applicants then validated their
resulting models by comparing their predictions of CRISPR-Cas
off-target cutting at multiple genomic loci with experimental data
assessing locus modification at the same sites.
The methodology adopted in developing this algorithm is as follows:
The problem summary states that for arbitrary spacers and targets
of constant length, a numerical model that makes thermodynamic
sense and predicts Cas9 cutting efficiency is to be found.
Suppose Cas9 modifies DNA:RNA hybridization free-energies locally
in a position-dependent but sequence-independent way. Then for
DNA:RNA hybridization free energies .DELTA.G.sub.ij(k) (for
position k between 1 and N) of spacer i and target j
.times..times..alpha..times..DELTA..times..times..function.
##EQU00004## Z.sub.ij can be treated as an "effective" free-energy
modified by the multiplicative position-weights .alpha..sub.k.
The "effective" free-energy Z.sub.ij corresponds to an associated
cutting-probability e.sup.-.beta.Z.sup.ij (for some constant
.beta.) in the same way that an equilibrium model of hybridization
(without position-weighting) would have predicted a
hybridization-probability .about.e.sup.-.beta..DELTA.G.sup.ij.
Since cutting-efficiency has been measured, the values Z.sub.ij can
be treated as their observables. Meanwhile, .DELTA.G.sub.ij(k) can
be calculated for any experiment's spacer-target pairing.
Applicants task was to find the values .alpha..sub.k, since this
would allow them to estimate Z.sub.ij for any spacer-target
pair.
Writing the above equation for Z.sub.ij in matrix form Applicants
get: {right arrow over (Z)}=G{right arrow over (.alpha.)} (1) The
least-squares estimate is then {right arrow over
(.alpha.)}.sub.est=(G.sup.TG).sup.-1G.sup.T{right arrow over (Z)}
where G.sup.T is the matrix-transpose of the G and
(G.sup.TG).sup.-1 is the inverse of their matrix-product.
In the above G is a matrix of local DNA:RNA free-energy values
whose rth row corresponds to experimental trial r and whose kth
column corresponds to the kth position in the DNA:RNA hybrid tested
in that experimental trial. {right arrow over (Z)} is meanwhile a
column-vector whose rth row corresponds to observables from the
same experimental trial as G's rth row. Because of the relation
described above wherein the CRISPR cutting frequencies are
estimated to vary as e.sup.-.beta.Z.sup.ij, these observables,
Z.sub.ij, were calculated as the natural logarithm of the observed
cutting frequency. The observable is the cleavage efficiency of
Cas, e.g., Cas9, at a target DNA for a particular guide RNA and
target DNA pair. The experiment is Cas, e.g., Cas9, with a
particular sgRNA/DNA target pairing, and the observable is the
cleavage percentage (whether measured as indel formation percentage
from cells or simply cleavage percentage in vitro) (see herein
discussion on generating training data set). More in particular,
every unique PCR reaction that was sequenced should be treated as a
unique experimental trial to encompass replicability within the
vector. This means that experimental replicates each go into
separate rows of equation 1 (and because of this, some rows of G
will be identical). The advantage of this is that when a is fit,
all relevant information--including replicability--is taken into
account in the final estimate.
Observable {right arrow over (Z)}, values were calculated as log
(observed frequency of cutting). Cutting frequencies were
normalized identically (so that they all have the same "units").
For plugging in sequencing indel-frequency values, it may be best,
however, to standardize sequencing depth.
The preferred way to do this would be to set a standard
sequencing-depth D for which all experiments included in {right
arrow over (Z)} have at least that number of reads. Since cutting
frequencies below 1/D cannot be consistently detected, this should
be set as the minimum frequency for the data-set, and the values in
{right arrow over (Z)} should range from log(1/D) to log(1). One
could vary the value of D later on to ensure that the {right arrow
over (.alpha.)} estimate isn't too dependent on the value
chosen.
In a further aspect, there are different methods of graphing NGG
and NNAGAAW sequences. One is with the `non-overlapping` method.
NGG and NRG may be regraphed in an "overlapping" fashion, as
indicated in FIGS. 6 A-C.
Applicants also performed a study on off target Cas9 activity as
indicated in FIGS. 10, 11 and 12. Aspects of the invention also
relate to predictive models that may not involve hybridization
energies but instead simply use the cutting frequency information
as a prediction (See FIG. 29).
Example 5
DNA Targeting Specificity of the RNA-Guided Cas9 Nuclease
Here, Applicants report optimization of various applications of
SpCas9 for mammalian genome editing and demonstrate that
SpCas9-mediated cleavage is unaffected by DNA methylation (FIG.
14). Applicants further characterize SpCas9 targeting specificity
using over 700 guide RNA variants and evaluate SpCas9-induced indel
mutation levels at over 100 predicted genomic off-target loci.
Contrary to previous models, Applicants found that SpCas9 tolerates
mismatches between guide RNA and target DNA at different positions
in a sequence-context dependent manner, sensitive to the number,
position and distribution of mismatches. Finally, Applicants
demonstrate that the dosage of SpCas9 and sgRNA can be titrated to
minimize off-target modification. To facilitate mammalian genome
engineering applications, Applicants used these results to
establish a computational platform to guide the selection and
validation of target sequences as well as off-target analyses.
The bacterial type II CRISPR system from S. pyogenes may be
reconstituted in mammalian cells using three minimal components:
the Cas9 nuclease (SpCas9), a specificity-determining CRISPR RNA
(crRNA), and an auxiliary trans-activating crRNA (tracrRNA).
Following crRNA and tracrRNA hybridization, SpCas9 is localized to
the genomic target matching a 20-nt guide sequence within the
crRNA, immediately upstream of a required 5'-NGG protospacer
adjacent motif (PAM). Each crRNA and tracrRNA duplex may also be
fused to generate a chimeric single guide RNA (sgRNA) that mimics
the natural crRNA-tracrRNA hybrid. Both crRNA-tracrRNA duplexes and
sgRNAs can be used to target SpCas9 for multiplexed genome editing
in eukaryotic cells.
Although an sgRNA design consisting of a truncated crRNA and
tracrRNA had been previously shown to mediate efficient cleavage in
vitro, it failed to achieve detectable cleavage at several loci
that were efficiently modified by crRNA-tracrRNA duplexes bearing
identical guide sequences. Because the major difference between
this sgRNA design and the native crRNA-tracrRNA duplex is the
length of the tracrRNA sequence, Applicants tested whether
extension of the tracrRNA tail was able to improve SpCas9
activity.
Applicants generated a set of sgRNAs targeting multiple sites
within the human EMX1 and PVALB loci with different tracrRNA 3'
truncations. Using the SURVEYOR nuclease assay, Applicants assessed
the ability of each Cas9 sgRNA complex to generate indels in HEK
293FT cells through the induction of DNA double-stranded breaks
(DSBs) and subsequent non-homologous end joining (NHEJ) DNA damage
repair (Methods and Materials). sgRNAs with +67 or +85 nucleotide
(nt) tracrRNA tails mediated DNA cleavage at all target sites
tested, with up to 5-fold higher levels of indels than the
corresponding crRNA-tracrRNA duplexes. Furthermore, both sgRNA
designs efficiently modified PVALB loci that were previously not
targetable using crRNA-tracrRNA duplexes. For all five tested
targets, Applicants observed a consistent increase in modification
efficiency with increasing tracrRNA length. Applicants performed
Northern blots for the guide RNA truncations and found increased
levels expression for the longer tracrRNA sequences, suggesting
that improved target cleavage was due to higher sgRNA expression or
stability. Taken together, these data indicate that the tracrRNA
tail is important for optimal SpCas9 expression and activity in
vivo.
Applicants further investigated the sgRNA architecture by extending
the duplex length from 12 to the 22 nt found in the native
crRNA-tracrRNA duplex. Applicants also mutated the sequence
encoding sgRNA to abolish any poly-T tracts that could serve as
premature transcriptional terminators for U6-driven transcription.
Applicants tested these new sgRNA scaffolds on 3 targets within the
human EMX1 gene and observed only modest changes in modification
efficiency. Thus, Applicants established sgRNA(+85), identical to
some sgRNAs previously used, as an effective SpCas9 guide RNA
architecture and used it in all subsequent studies.
Applicants have previously shown that a catalytic mutant of SpCas9
(D10A nickase) can mediate gene editing by homology-directed repair
(HR) without detectable indel formation. Given its higher cleavage
efficiency, Applicants tested whether sgRNA(+85), in complex with
the Cas9 nickase, can likewise facilitate HR without incurring
on-target NHEJ. Using single-stranded oligonucleotides (ssODNs) as
repair templates, Applicants observed that both the wild-type and
the D10A SpCas9 mediate HR in HEK 293FT cells, while only the
former is able to do so in human embryonic stem cells. Applicants
further confirmed using SURVEYOR assay that no target indel
mutations are induced by the SpCas9 D10A nickase.
To explore whether the genome targeting ability of sgRNA(+85) is
influenced by epigenetic factors that constrain the alternative
transcription activator-like effector nuclease (TALENs) and
potentially also zinc finger nuclease (ZFNs) technologies,
Applicants further tested the ability of SpCas9 to cleave
methylated DNA. Using either unmethylated or M. SssI-methylated
pUC19 as DNA targets (FIG. 14a,b) in a cell-free cleavage assay,
Applicants showed that SpCas9 efficiently cleaves pUC19 regardless
of CpG methylation status in either the 20-bp target sequence or
the PAM (FIG. 14c). To test whether this is also true in vivo,
Applicants designed sgRNAs to target a highly methylated region of
the human SERPINB5 locus. All three sgRNAs tested were able to
mediate indel mutations in endogenously methylated targets.
Having established the optimal guide RNA architecture for SpCas9
and demonstrated its insensitivity to genomic CpG methylation,
Applicants sought to conduct a comprehensive characterization of
the DNA targeting specificity of SpCas9. Previous studies on SpCas9
cleavage specificity were limited to a small set of
single-nucleotide mismatches between the guide sequence and DNA
target, suggesting that perfect base-pairing within 10-12 bp
directly 5' of PAM determines Cas9 specificity, whereas PAM-distal
multiple mismatches can be tolerated. In addition, a recent study
using catalytically inactive SpCas9 as a transcriptional repressor
found no significant off-target effects throughout the E. coli
transcriptome. However, a systematic analysis of Cas9 specificity
within the context of a larger mammalian genome has not yet been
reported.
To address this, Applicants first evaluated the effect of imperfect
guide RNA identity for targeting genomic DNA on SpCas9 activity,
and then assessed the cleavage activity resulting from a single
sgRNA on multiple genomic off-target loci with sequence similarity.
To facilitate large scale testing of mismatched guide sequences,
Applicants developed a simple sgRNA testing assay by generating
expression cassettes encoding U6-driven sgRNAs by PCR and
transfecting the resulting amplicons. Applicants then performed
deep sequencing of the region flanking each target site for two
independent biological replicates. From these data, Applicants
applied a binomial model to detect true indel events resulting from
SpCas9 cleavage and NHEJ misrepair and calculated 95% confidence
intervals for all reported NHEJ frequencies.
Applicants used a linear model of free energy position-dependence
to investigate the combined contribution of DNA:RNA sequence and
mismatch-location on Cas9 cutting efficiency. While sequence
composition and mismatch location alone generated Spearman
correlations between estimated and observed cutting efficiencies
for EMX1 target site 1 and 0.78, respectively, integration of the
two parameters greatly improved this agreement, with Spearman
correlation 0.86 (p<0.001). Furthermore, the incorporation of
nupac RNA:RNA hybridization energies into Applicants' free energy
model resulted in a 10% increase in the Spearman correlation
coefficient. Taken together, the data suggests an effect of
SpCas9-specific perturbations on the Watson-Crick base-pairing free
energies. Meanwhile, sequence composition did not substantially
improve agreement between estimated and observed cutting
efficiencies for EMX1 target site 6 (Spearman correlation 0.91,
p<0.001). This suggested that single mismatches in EMX1 target
site 6 contributed minimally to the thermodynamic binding free
energy itself.
Potential genomic off-target sites with sequence similarity to a
target site of interest may often have multiple base mismatches.
Applicants designed a set of guide RNAs for EMX1 targets 1 and 6
that contains different combinations of mismatches to investigate
the effect of mismatch number, position, and spacing on Cas9 target
cleavage activity (FIG. 13a,b).
By concatenating blocks of mismatches, Applicants found that two
consecutive mismatches within the PAM-proximal sequence reduced
Cas9 cutting for both targets to <1% (FIG. 13a; top panels).
Target site 1 cutting increased as the double mismatches shifted
distally from the PAM, whereas observed cleavage for target site 6
consistently remained <0.5%. Blocks of three or five consecutive
mismatches for both targets diminished Cas9 cutting to levels
<0.5% regardless of position (FIG. 13, lower panels).
To investigate the effect of mismatch spacing, Applicants anchored
a single PAM-proximal mutation while systematically increasing the
separation between subsequent mismatches. Groups of 3 or 4
mutations each separated by 3 or fewer bases diminished Cas9
nuclease activity to levels <0.5%. However, Cas9 cutting at
target site 1 increased to 3-4% when the mutations were separated
by 4 or more unmutated bases (FIG. 13b). Similarly, groups of 4
mutations separated by 4 or more bases led to indel efficiencies
from 0.5-1%. However, cleavage at target site 6 consistently
remained below 0.5% regardless of the number or spacing of the
guide RNA mismatches.
The multiple guide RNA mismatch data indicate that increasing the
number of mutations diminishes and eventually abolishes cleavage.
Unexpectedly, isolated mutations are tolerated as separation
increased between each mismatch. Consistent with the single
mismatch data, multiple mutations within the PAM-distal region are
generally tolerated by Cas9 while clusters of PAM-proximal
mutations are not. Finally, although the mismatch combinations
represent a limited subset of base mutations, there appears to be
target-specific susceptibility to guide RNA mismatches. For
example, target site 6 generally showed lower cleavage with
multiple mismatches, a property also reflected in its longer 12-14
bp PAM-proximal region of mutation intolerance (FIG. 12). Further
investigation of Cas9 sequence-specificity may reveal design
guidelines for choosing more specific DNA targets.
To determine if Applicants' findings from the guide RNA mutation
data generalize to target DNA mismatches and allow the prediction
of off-target cleavage within the genome, Applicants transfected
cells with Cas9 and guide RNAs targeting either target 3 or target
6, and performed deep sequencing of candidate off-target sites with
sequence similarity. No genomic loci with only 1 mismatch to either
targets was identified. Genomic loci containing 2 or 3 mismatches
relative to target 3 or target 6 revealed cleavage at some of the
off-targets assessed (FIG. 13c). Targets 3 and 6 exhibited cleavage
efficiencies of 7.5% and 8.0%, whereas off-target sites 3-1, 3-2,
3-4, and 3-5 were modified at 0.19%, 0.42%, 0.97%, and 0.50%,
respectively. All other off-target sites cleaved at under 0.1% or
were modified at levels indistinguishable from sequencing error.
The off-target cutting rates were consistent with the collective
results from the guide RNA mutation data: cleavage was observed at
a small subset of target 3 off-targets that contained either very
PAM-distal mismatches or had single mismatches separated by 4 or
more bases.
Given that the genome targeting efficiencies of TALENs and ZFNs may
be sensitive to confounding effects such as chromatin state or DNA
methylation, Applicants sought to test whether RNA-guided SpCas9
cleavage activity would be affected by the epigenetic state of a
target locus. To test this, Applicants methylated a plasmid in
vitro and performed an in vitro cleavage assay on two pairs of
targets containing either unmethylated or methylated CpGs. SpCas9
mediated efficient cleavage of the plasmid whether methylation
occurred in the target proper or within the PAM, suggesting that
SpCas9 may not be susceptible to DNA methylation effects.
The ability to program Cas9 to target specific sites in the genome
by simply designing a short sgRNA has enormous potential for a
variety of applications. Applicants' results demonstrate that the
specificity of Cas9-mediated DNA cleavage is sequence-dependent and
is governed not only by the location of mismatching bases, but also
by their spacing. Importantly, while the PAM-proximal 9-12 nt of
the guide sequence generally defines specificity, the PAM-distal
sequences also contribute to the overall specificity of
Cas9-mediated DNA cleavage. Although there are off-target cleavage
sites for a given guide sequence, expected off-target sites are
likely predictable based on their mismatch locations. Further work
looking at the thermodynamics of sgRNA-DNA interaction will likely
yield additional predictive power for off-target activity, and
exploration of alternative Cas9 orthologs may also yield novel
variants of Cas9s with improved specificity. Taken together, the
high efficiency of Cas9 as well as its low off-target activity make
CRISPR-Cas an attractive genome engineering technology.
Example 6
Use of Cas9 to Target a Variety of Disease Types
The specificity of Cas9 orthologs can be evaluated by testing the
ability of each Cas9 to tolerate mismatches between the guide RNA
and its DNA target. For example, the specificity of SpCas9 has been
characterized by testing the effect of mutations in the guide RNA
on cleavage efficiency. Libraries of guide RNAs were made with
single or multiple mismatches between the guide sequence and the
target DNA. Based on these findings, target sites for SpCas9 can be
selected based on the following guidelines:
To maximize SpCas9 specificity for editing a particular gene, one
should choose a target site within the locus of interest such that
potential `off-target` genomic sequences abide by the following
four constraints: First and foremost, they should not be followed
by a PAM with either 5'-NGG or NAG sequences. Second, their global
sequence similarity to the target sequence should be minimized.
Third, a maximal number of mismatches should lie within the
PAM-proximal region of the off-target site. Finally, a maximal
number of mismatches should be consecutive or spaced less than four
bases apart.
Similar methods can be used to evaluate the specificity of other
Cas9 orthologs and to establish criteria for the selection of
specific target sites within the genomes of target species.
Target selection for sgRNA: There are two main considerations in
the selection of the 20-nt guide sequence for gene targeting: 1)
the target sequence should precede the 5'-NGG PAM for S. pyogenes
Cas9, and 2) guide sequences should be chosen to minimize
off-target activity. Applicants provided an online Cas9 targeting
design tool (available at the website genome-engineering.org/tools;
see Examples above and FIG. 23) that takes an input sequence of
interest and identifies suitable target sites. To experimentally
assess off-target modifications for each sgRNA, Applicants also
provide computationally predicted off-target sites for each
intended target, ranked according to Applicants" quantitative
specificity analysis on the effects of base-pairing mismatch
identity, position, and distribution.
The detailed information on computationally predicted off-target
sites is as follows: Considerations for Off-target Cleavage
Activities: Similar to other nucleases, Cas9 can cleave off-target
DNA targets in the genome at reduced frequencies. The extent to
which a given guide sequence exhibit off-target activity depends on
a combination of factors including enzyme concentration,
thermodynamics of the specific guide sequence employed, and the
abundance of similar sequences in the target genome. For routine
application of Cas9, it is important to consider ways to minimize
the degree of off-target cleavage and also to be able to detect the
presence of off-target cleavage.
Minimizing off-target activity: For application in cell lines,
Applicants recommend following two steps to reduce the degree of
off-target genome modification. First, using Applicants' online
CRISPR target selection tool, it is possible to computationally
assess the likelihood of a given guide sequence to have off-target
sites. These analyses are performed through an exhaustive search in
the genome for off-target sequences that are similar sequences as
the guide sequence. Comprehensive experimental investigation of the
effect of mismatching bases between the sgRNA and its target DNA
revealed that mismatch tolerance is 1) position dependent--the 8-14
bp on the 3' end of the guide sequence are less tolerant of
mismatches than the 5' bases, 2) quantity dependent--in general
more than 3 mismatches are not tolerated, 3) guide sequence
dependent--some guide sequences are less tolerant of mismatches
than others, and 4) concentration dependent--off-target cleavage is
highly sensitive to the amount of transfected DNA. The Applicants'
target site analysis web tool (available at the website
genome-engineering.org/tools) integrates these criteria to provide
predictions for likely off-target sites in the target genome.
Second, Applicants recommend titrating the amount of Cas9 and sgRNA
expression plasmid to minimize off-target activity.
Detection of off-target activities: Using Applicants' CRISPR
targeting web tool, it is possible to generate a list of most
likely off-target sites as well as primers performing SURVEYOR or
sequencing analysis of those sites. For isogenic clones generated
using Cas9, Applicants strongly recommend sequencing these
candidate off-target sites to check for any undesired mutations. It
is worth noting that there may be off target modifications in sites
that are not included in the predicted candidate list and full
genome sequence should be performed to completely verify the
absence of off-target sites. Furthermore, in multiplex assays where
several DSBs are induced within the same genome, there may be low
rates of translocation events and can be evaluated using a variety
of techniques such as deep sequencing (48).
The online tool (FIG. 23) provides the sequences for all oligos and
primers necessary for 1) preparing the sgRNA constructs, 2)
assaying target modification efficiency, and 3) assessing cleavage
at potential off-target sites. It is worth noting that because the
U6 RNA polymerase III promoter used to express the sgRNA prefers a
guanine (G) nucleotide as the first base of its transcript, an
extra G is appended at the 5' of the sgRNA where the 20-nt guide
sequence does not begin with G (FIG. 24).
Example 7
Base Pair Mismatching Investigations
Applicants tested whether extension of the tracrRNA tail was able
to improve SpCas9 activity. Applicants generated a set of sgRNAs
targeting multiple sites within the human EMX1 and PVALB loci with
different tracrRNA 3' truncations (FIG. 9a). Using the SURVEYOR
nuclease assay, Applicants assessed the ability of each Cas9 sgRNA
complex to generate indels in HEK 293FT cells through the induction
of DNA double-stranded breaks (DSBs) and subsequent non-homologous
end joining (NHEJ) DNA damage repair (Methods and Materials).
sgRNAs with +67 or +85 nucleotide (nt) tracrRNA tails mediated DNA
cleavage at all target sites tested, with up to 5-fold higher
levels of indels than the corresponding crRNA-tracrRNA duplexes
(FIG. 9). Furthermore, both sgRNA designs efficiently modified
PVALB loci that were previously not targetable using crRNA-tracrRNA
duplexes (1) (FIG. 9b and FIG. 9b). For all five tested targets,
Applicants observed a consistent increase in modification
efficiency with increasing tracrRNA length. Applicants performed
Northern blots for the guide RNA truncations and found increased
levels expression for the longer tracrRNA sequences, suggesting
that improved target cleavage was due to higher sgRNA expression or
stability (FIG. 9c). Taken together, these data indicate that the
tracrRNA tail is important for optimal SpCas9 expression and
activity in vivo.
Applicants have previously shown that a catalytic mutant of SpCas9
(D10A nickase) can mediate gene editing by homology-directed repair
(HR) without detectable indel formation. Given its higher cleavage
efficiency, Applicants tested whether sgRNA(+85), in complex with
the Cas9 nickase, can likewise facilitate HR without incurring
on-target NHEJ. Using single-stranded oligonucleotides (ssODNs) as
repair templates, Applicants observed that both the wild-type and
the D10A SpCas9 mediate HR in HEK 293FT cells, while only the
former is able to do so in human embryonic stem cells (hESCs; FIG.
9d).
To explore whether the genome targeting ability of sgRNA(+85) is
influenced by epigenetic factors that constrain the alternative
transcription activator-like effector nuclease (TALENs) and
potentially also zinc finger nuclease (ZFNs) technologies,
Applicants further tested the ability of SpCas9 to cleave
methylated DNA. Using either unmethylated or M. SssI-methylated
pUC19 as DNA targets (FIG. 14a,b) in a cell-free cleavage assay,
Applicants showed that SpCas9 efficiently cleaves pUC19 regardless
of CpG methylation status in either the 20-bp target sequence or
the PAM. To test whether this is also true in vivo, Applicants
designed sgRNAs to target a highly methylated region of the human
SERPINB5 locus (FIG. 9e,f). All three sgRNAs tested were able to
mediate indel mutations in endogenously methylated targets (FIG.
9g).
Applicants systematically investigated the effect of base-pairing
mismatches between guide RNA sequences and target DNA on target
modification efficiency. Applicants chose four target sites within
the human EMX1 gene and, for each, generated a set of 57 different
guide RNAs containing all possible single nucleotide substitutions
in positions 1-19 directly 5' of the requisite NGG PAM (FIG. 25a).
The 5' guanine at position 20 is preserved, given that the U6
promoter requires guanine as the first base of its transcript.
These `off-target` guide RNAs were then assessed for cleavage
activity at the on-target genomic locus.
Consistent with previous findings, SpCas9 tolerates single base
mismatches in the PAM-distal region to a greater extent than in the
PAM-proximal region. In contrast with a model that implies a
prototypical 10-12 bp PAM-proximal seed sequence that determines
target specificity, Applicants found that most bases within the
target site are specifically recognized, although mismatches are
tolerated at different positions in a sequence-context dependent
manner. Single-base specificity generally ranges from 8 to 12 bp
immediately upstream of the PAM, indicating a sequence-dependent
specificity boundary that varies in length (FIG. 25b).
To further investigate the contributions of base identity and
position within the guide RNA to SpCas9 specificity, Applicants
generated additional sets of mismatched guide RNAs for eleven more
target sites within the EMX1 locus (FIG. 28) totaling over 400
sgRNAs. These guide RNAs were designed to cover all 12 possible
RNA:DNA mismatches for each position in the guide sequence with at
least 2.times. coverage for positions 1-10. Applicants' aggregate
single mismatch data reveals multiple exceptions to the seed
sequence model of SpCas9 specificity (FIG. 25c). In general,
mismatches within the 8-12 PAM-proximal bases were less tolerated
by SpCas9, whereas those in the PAM-distal regions had little
effect on SpCas9 cleavage. Within the PAM-proximal region, the
degree of tolerance varied with the identity of a particular
mismatch, with rC:dC base-pairing exhibiting the highest level of
disruption to SpCas9 cleavage (FIG. 25c).
In addition to the target specificity, Applicants also investigated
the NGG PAM requirement of SpCas9. To vary the second and third
positions of PAM, Applicants selected 32 target sites within the
EMX1 locus encompassing all 16 possible alternate PAMs with
2.times. coverage (Table 4). Using SURVEYOR assay, Applicants
showed that SpCas9 also cleaves targets with NAG PAMs, albeit
5-fold less efficiently than target sites with NGG PAMs (FIG. 25d).
The tolerance for an NAG PAM is in agreement with previous
bacterial studies (12) and expands the S. pyogenes Cas9 target
space to every 4-bp on average within the human genome, not
accounting for constraining factors such as guide RNA secondary
structure or certain epigenetic modifications (FIG. 25e).
Applicants next explored the effect of multiple base mismatches on
SpCas9 target activity. For four targets within the EMX1 gene,
Applicants designed sets of guide RNAs that contained varying
combinations of mismatches to investigate the effect of mismatch
number, position, and spacing on SpCas9 target cleavage activity
(FIG. 26a, b).
In general, Applicants observed that the total number of mismatched
base-pairs is a key determinant for SpCas9 cleavage efficiency. Two
mismatches, particularly those occurring in a PAM-proximal region,
significantly reduced SpCas9 activity whether these mismatches are
concatenated or interspaced (FIG. 26a, b); this effect is further
magnified for three concatenated mismatches (FIG. 20a).
Furthermore, three or more interspaced (FIG. 26c) and five
concatenated (FIG. 26a) mismatches eliminated detectable SpCas9
cleavage in the vast majority of loci.
The position of mismatches within the guide sequence also affected
the activity of SpCas9: PAM-proximal mismatches are less tolerated
than PAM-distal counterparts (FIG. 26a), recapitulating Applicants'
observations from the single base-pair mismatch data (FIG. 25c).
This effect is particularly salient in guide sequences bearing a
small number of total mismatches, whether those are concatenated
(FIG. 26a) or interspaced (FIG. 26b). Additionally, guide sequences
with mismatches spaced four or more bases apart also mediated
SpCas9 cleavage in some cases (FIG. 26c). Thus, together with the
identity of mismatched base-pairing, Applicants observed that many
off-target cleavage effects can be explained by a combination of
mismatch number and position.
Given these mismatched guide RNA results, Applicants expected that
for any particular sgRNA, SpCas9 may cleave genomic loci that
contain small numbers of mismatched bases. For the four EMX1
targets described above, Applicants computationally identified 117
candidate off-target sites in the human genome that are followed by
a 5'-NRG PAM and meet any of the additional following criteria: 1.
up to 5 mismatches, 2. short insertions or deletions, or 3.
mismatches only in the PAM-distal region. Additionally, Applicants
assessed off-target loci of high sequence similarity without the
PAM requirement. The majority of off-target sites tested for each
sgRNA (30/31, 23/23, 48/51, and 12/12 sites for EMX1 targets 1, 2,
3, and 6, respectively) exhibited modification efficiencies at
least 100-fold lower than that of corresponding on-targets (FIG.
27a, b). Of the four off-target sites identified, three contained
only mismatches in the PAM-distal region, consistent with the
Applicants' multiple mismatch sgRNA observations (FIG. 26).
Notably, these three loci were followed by 5'-NAG PAMs,
demonstrating that off-target analyses of SpCas9 must include
5'-NAG as well as 5'-NGG candidate loci.
Enzymatic specificity and activity strength are often highly
dependent on reaction conditions, which at high reaction
concentration might amplify off-target activity (26, 27). One
potential strategy for minimizing non-specific cleavage is to limit
the enzyme concentration, namely the level of SpCas9-sgRNA complex.
Cleavage specificity, measured as a ratio of on- to off-target
cleavage, increased dramatically as Applicants decreased the
equimolar amounts of SpCas9 and sgRNA transfected into 293FT cells
(FIG. 27c, d) from 7.1.times.10-10 to 1.8.times.10-11 nmoL/cell
(400 ng to 10 ng of Cas9-sgRNA plasmid). qRT-PCR assay confirmed
that the level of hSpCas9 mRNA and sgRNA decreased proportionally
to the amount of transfected DNA. Whereas specificity increased
gradually by nearly 4-fold as Applicants decreased the transfected
DNA amount from 7.1.times.10-10 to 9.0.times.10-11 nmol/cell (400
ng to 50 ng plasmid), Applicants observed a notable additional
7-fold increase in specificity upon decreasing transfected DNA from
9.0.times.10-11 to 1.8.times.10-11 nmol/cell (50 ng to 10 ng
plasmid; FIG. 27c). These findings suggest that Applicants may
minimize the level of off-target activity by titrating the amount
of SpCas9 and sgRNA DNA delivered. However, increasing specificity
by reducing the amount of transfected DNA also leads to a reduction
in on-target cleavage. These measurements enable quantitative
integration of specificity and efficiency criteria into dosage
choice to optimize SpCas9 activity for different applications.
Applicants further explore modifications in SpCas9 and sgRNA design
that may improve the intrinsic specificity without sacrificing
cleavage efficiency. FIG. 29 shows data for EMX1 target 2 and
target 6. For the tested sites in FIGS. 27 and 29 (in this case,
sites with 3 mismatches or less), there were no off-target sites
identified (defined as off-target site cleavage within 100-fold of
the on-target site cleavage).
The ability to program SpCas9 to target specific sites in the
genome by simply designing a short sgRNA holds enormous potential
for a variety of applications. Applicants' results demonstrate that
the specificity of SpCas9-mediated DNA cleavage is sequence- and
locus-dependent and governed by the quantity, position, and
identity of mismatching bases. Importantly, while the PAM-proximal
8-12 bp of the guide sequence generally defines specificity, the
PAM-distal sequences also contribute to the overall specificity of
SpCas9-mediated DNA cleavage. Although there may be off-target
cleavage for a given guide sequence, they can be predicted and
likely minimized by following general design guidelines.
To maximize SpCas9 specificity for editing a particular gene, one
should identify potential `off-target` genomic sequences by
considering the following four constraints: First and foremost,
they should not be followed by a PAM with either 5'-NGG or 5'-NAG
sequences. Second, their global sequence similarity to the target
sequence should be minimized, and guide sequences with genomic
off-target loci that have fewer than 3 mismatches should be
avoided. Third, at least 2 mismatches should lie within the
PAM-proximal region of the off-target site. Fourth, a maximal
number of mismatches should be consecutive or spaced less than four
bases apart. Finally, the amount of SpCas9 and sgRNA may be
titrated to optimize on- to off-target cleavage ratio.
Using these criteria, Applicants formulated a simple scoring scheme
to integrate the contributions of mismatch location, density, and
identity for quantifying their contribution to SpCas9 cutting.
Applicants applied the aggregate cleavage efficiencies of
single-mismatch guide RNAs to test this scoring scheme separately
on genome-wide targets. Applicants found that these factors, taken
together, accounted for more than 50% of the variance in
cutting-frequency rank among the genome-wide targets studied (FIG.
30).
Implementing the guidelines delineated above, Applicants designed a
computational tool to facilitate the selection and validation of
sgRNAs as well as to predict off-target loci for specificity
analyses; this tool may be accessed at the website
genome-engineering.org/tools. These results and tools further
extend the SpCas9 system as a powerful and versatile alternative to
ZFNs and TALENs for genome editing applications. Further work
examining the thermodynamics and in vivo stability of sgRNA-DNA
duplexes will likely yield additional predictive power for
off-target activity, while exploration of SpCas9 mutants and
orthologs may yield novel variants with improved specificity.
Accession codes All raw reads can be accessed at NCBI BioProject,
accession number SRP023129.
Methods and Materials:
Cell culture and transfection--Human embryonic kidney (HEK) cell
line 293FT (Life Technologies) was maintained in Dulbecco's
modified Eagle's Medium (DMEM) supplemented with 10% fetal bovine
serum (HyClone), 2 mM GlutaMAX (Life Technologies), 100 U/mL
penicillin, and 100 .mu.g/mL streptomycin at 37.degree. C. with 5%
CO2 incubation.
293FT cells were seeded either onto 6-well plates, 24-well plates,
or 96-well plates (Corning) 24 hours prior to transfection. Cells
were transfected using Lipofectamine 2000 (Life Technologies) at
80-90% confluence following the manufacturer's recommended
protocol. For each well of a 6-well plate, a total of 1 ug of
Cas9+sgRNA plasmid was used. For each well of a 24-well plate, a
total of 500 ng Cas9+sgRNA plasmid was used unless otherwise
indicated. For each well of a 96-well plate, 65 ng of Cas9 plasmid
was used at a 1:1 molar ratio to the U6-sgRNA PCR product.
Human embryonic stem cell line HUES9 (Harvard Stem Cell Institute
core) was maintained in feeder-free conditions on GelTrex (Life
Technologies) in mTesR medium (Stemcell Technologies) supplemented
with 100 ug/ml Normocin (InvivoGen). HUES9 cells were transfected
with Amaxa P3 Primary Cell 4-D Nucleofector Kit (Lonza) following
the manufacturer's protocol.
SURVEYOR Nuclease Assay for Genome Modification
293FT cells were transfected with plasmid DNA as described above.
Cells were incubated at 37.degree. C. for 72 hours
post-transfection prior to genomic DNA extraction. Genomic DNA was
extracted using the QuickExtract DNA Extraction Solution
(Epicentre) following the manufacturer's protocol. Briefly,
pelleted cells were resuspended in QuickExtract solution and
incubated at 65.degree. C. for 15 minutes and 98.degree. C. for 10
minutes.
The genomic region flanking the CRISPR target site for each gene
was PCR amplified (primers listed in Table 2), and products were
purified using QiaQuick Spin Column (Qiagen) following the
manufacturer's protocol. 400 ng total of the purified PCR products
were mixed with 2 .mu.l 10.times.Taq DNA Polymerase PCR buffer
(Enzymatics) and ultrapure water to a final volume of 20 .mu.l, and
subjected to a re-annealing process to enable heteroduplex
formation: 95.degree. C. for 10 min, 95.degree. C. to 85.degree. C.
ramping at -2.degree. C./s, 85.degree. C. to 25.degree. C. at
-0.25.degree. C./s, and 25.degree. C. hold for 1 minute. After
re-annealing, products were treated with SURVEYOR nuclease and
SURVEYOR enhancer S (Transgenomics) following the manufacturer's
recommended protocol, and analyzed on 4-20% Novex TBE
poly-acrylamide gels (Life Technologies). Gels were stained with
SYBR Gold DNA stain (Life Technologies) for 30 minutes and imaged
with a Gel Doc gel imaging system (Bio-rad). Quantification was
based on relative band intensities.
Northern blot analysis of tracrRNA expression in human cells:
Northern blots were performed as previously described 1. Briefly,
RNAs were heated to 95.degree. C. for 5 min before loading on 8%
denaturing polyacrylamide gels (SequaGel, National Diagnostics).
Afterwards, RNA was transferred to a pre-hybridized Hybond N+
membrane (GE Healthcare) and crosslinked with Stratagene UV
Crosslinker (Stratagene). Probes were labeled with [gamma-32P] ATP
(Perkin Elmer) with T4 polynucleotide kinase (New England Biolabs).
After washing, membrane was exposed to phosphor screen for one hour
and scanned with phosphorimager (Typhoon).
Bisulfite sequencing to assess DNA methylation status: HEK 293FT
cells were transfected with Cas9 as described above. Genomic DNA
was isolated with the DNeasy Blood & Tissue Kit (Qiagen) and
bisulfite converted with EZ DNA Methylation-Lightning Kit (Zymo
Research). Bisulfite PCR was conducted using KAPA2G Robust HotStart
DNA Polymerase (KAPA Biosystems) with primers designed using the
Bisulfite Primer Seeker (Zymo Research, Table 6). Resulting PCR
amplicons were gel-purified, digested with EcoRI and HindIII, and
ligated into a pUC19 backbone prior to transformation. Individual
clones were then Sanger sequenced to assess DNA methylation
status.
In vitro transcription and cleavage assay: HEK 293FT cells were
transfected with Cas9 as described above. Whole cell lysates were
then prepared with a lysis buffer (20 mM HEPES, 100 mM KCl, 5 mM
MgCl2, 1 mM DTT, 5% glycerol, 0.1% Triton X-100) supplemented with
Protease Inhibitor Cocktail (Roche). T7-driven sgRNA was in vitro
transcribed using custom oligos (Sequences) and HiScribe T7 In
Vitro Transcription Kit (NEB), following the manufacturer's
recommended protocol. To prepare methylated target sites, pUC19
plasmid was methylated by M.SssI and then linearized by NheI. The
in vitro cleavage assay was performed as follows: for a 20 uL
cleavage reaction, 10 uL of cell lysate with incubated with 2 uL
cleavage buffer (100 mM HEPES, 500 mM KCl, 25 mM MgCl2, 5 mM DTT,
25% glycerol), the in vitro transcribed RNA, and 300 ng pUC19
plasmid DNA.
Deep sequencing to assess targeting specificity: HEK 293FT cells
plated in 96-well plates were transfected with Cas9 plasmid DNA and
single guide RNA (sgRNA) PCR cassette 72 hours prior to genomic DNA
extraction (FIG. 14). The genomic region flanking the CRISPR target
site for each gene was amplified by a fusion PCR method to attach
the Illumina P5 adapters as well as unique sample-specific barcodes
to the target amplicons. PCR products were purified using EconoSpin
96-well Filter Plates (Epoch Life Sciences) following the
manufacturer's recommended protocol.
Barcoded and purified DNA samples were quantified by Quant-iT
PicoGreen dsDNA Assay Kit or Qubit 2.0 Fluorometer (Life
Technologies) and pooled in an equimolar ratio. Sequencing
libraries were then deep sequenced with the Illumina MiSeq Personal
Sequencer (Life Technologies).
Sequencing data analysis and indel detection: MiSeq reads were
filtered by requiring an average Phred quality (Q score) of at
least 23, as well as perfect sequence matches to barcodes and
amplicon forward primers. Reads from on- and off-target loci were
analyzed by first performing Smith-Waterman alignments against
amplicon sequences that included 50 nucleotides upstream and
downstream of the target site (a total of 120 bp). Alignments,
meanwhile, were analyzed for indels from 5 nucleotides upstream to
5 nucleotides downstream of the target site (a total of 30 bp).
Analyzed target regions were discarded if part of their alignment
fell outside the MiSeq read itself, or if matched base-pairs
comprised less than 85% of their total length.
Negative controls for each sample provided a gauge for the
inclusion or exclusion of indels as putative cutting events. For
each sample, an indel was counted only if its quality score
exceeded .mu.-.sigma., where .mu. was the mean quality-score of the
negative control corresponding to that sample and .sigma. was the
standard deviation of same. This yielded whole target-region indel
rates for both negative controls and their corresponding samples.
Using the negative control's per-target-region-per-read error rate,
q, the sample's observed indel count n, and its read-count R, a
maximum-likelihood estimate for the fraction of reads having
target-regions with true-indels, p, was derived by applying a
binomial error model, as follows.
Letting the (unknown) number of reads in a sample having target
regions incorrectly counted as having at least 1 indel be E,
Applicants can write (without making any assumptions about the
number of true indels)
.function..function..times..function..function. ##EQU00005## since
R(1-p) is the number of reads having target-regions with no true
indels. Meanwhile, because the number of reads observed to have
indels is n, n=E+Rp, in other words the number of reads having
target-regions with errors but no true indels plus the number of
reads whose target-regions correctly have indels. Applicants can
then re-write the above
.function..function..function..times..function. ##EQU00006##
Taking all values of the frequency of target-regions with
true-indels p to be equally probable a priori,
Prob(n|p).varies.Prob(p|n). The maximum-likelihood estimate (MLE)
for the frequency of target regions with true-indels was therefore
set as the value of p that maximized Prob(n|p). This was evaluated
numerically.
In order to place error bounds on the true-indel read frequencies
in the sequencing libraries themselves, Wilson score intervals (2)
were calculated for each sample, given the MLE-estimate for
true-indel target-regions, Rp, and the number of reads R.
Explicitly, the lower bound l and upper bound u were calculated
as
.times..function. ##EQU00007## .times..function. ##EQU00007.2##
where z, the standard score for the confidence required in normal
distribution of variance 1, was set to 1.96, meaning a confidence
of 95%.
qRT-PCR analysis of relative Cas9 and sgRNA expression: 293FT cells
plated in 24-well plates were transfected as described above. 72
hours post-transfection, total RNA was harvested with miRNeasy
Micro Kit (Qiagen). Reverse-strand synthesis for sgRNAs was
performed with qScript Flex cDNA kit (VWR) and custom first-strand
synthesis primers (Table 6). qPCR analysis was performed with Fast
SYBR Green Master Mix (Life Technologies) and custom primers (Table
2), using GAPDH as an endogenous control. Relative quantification
was calculated by the .DELTA..DELTA.CT method.
TABLE-US-00007 TABLE 1 Target site sequences. Tested target sites
for S. pyogenes type II CRISPR system with the requisite PAM. Cells
were transfected with Cas9 and either crRNA-tracrRNA or chimeric
sgRNA for each target. Target site genomic Target site sequence ID
target (5' to 3') PAM strand 1 EMX1 GTCACCTCCAATGACTAGGG TGG + 2
EMX1 GACATCGATGTCCTCCCCAT TGG - 3 EMX1 GAGTCCGAGCAGAAGAAGAA GGG + 6
EMX1 GCGCCACCGGTTGATGTGAT GGG - 10 EMX1 GGGGCACAGATGAGAAACTC AGG -
11 EMX1 GTACAAACGGCAGAAGCTGG AGG + 12 EMX1 GGCAGAAGCTGGAGGAGGAA GGG
+ 13 EMX1 GGAGCCCTTCTTCTTCTGCT CGG - 14 EMX1 GGGCAACCACAAACCCACGA
GGG + 15 EMX1 GCTCCCATCACATCAACCGG TGG + 16 EMX1
GTGGCGCATTGCCACGAAGC AGG + 17 EMX1 GGCAGAGTGCTGCTTGCTGC TGG + 18
EMX1 GCCCCTGCGTGGGCCCAAGC TGG + 19 EMX1 GAGTGGCCAGAGTCCAGCTT GGG -
20 EMX1 GGCCTCCCCAAAGCCTGGCC AGG - 4 PVALB GGGGCCGAGATTGGGTGTTC AGG
+ 5 PVALB GTGGCGAGAGGGGCCGAGAT TGG + 1 SERPINB5
GAGTGCCGCCGAGGCGGGGC GGG + 2 SERPINB5 GGAGTGCCGCCGAGGCGGGG CGG + 3
SERPINB5 GGAGAGGAGTGCCGCCGAGG CGG +
TABLE-US-00008 TABLE 2 Primer sequences SURVEYOR assay genomic
primer sequence primer name target (5' to 3') Sp-EMX1-F1 EMX1
AAAACCACCCTTCTCTCTGGC Sp-EMX1-R1 EMX1 GGAGATTGGAGACACGGAGAG
Sp-EMX1-F2 EMX1 CCATCCCCTTCTGTGAATGT Sp-EMX1-R2 EMX1
GGAGATTGGAGACACGGAGA Sp-PVALB-F PVALB CTGGAAAGCCAATGCCTGAC
Sp-PVALB-R PVALB GGCAGCAAACTCCTTGTCCT primer sequence primer name
(5' to 3') qRT-PCR for Cas9 and sgRNA expression sgRNA reverse-
AAGCACCGACTCGGTGCCAC strand synthesis EMX1.1 sgRNA
TCACCTCCAATGACTAGGGG qPCR F EMX1.1 sgRNA CAAGTTGATAACGGACTAGCCT
qPCR R EMX1.3 sgRNA AGTCCGAGCAGAAGAAGAAGTTT qPCR F EMX1.3 sgRNA
TTTCAAGTTGATAACGGACTAGCCT qPCR R Cas9 qPCR F AAACAGCAGATTCGCCTGGA
Cas9 qPCR R TCATCCGCTCGATGAAGCTC GAPDH qPCR F TCCAAAATCAAGTGGGGCGA
GAPDH qPCR R TGATGACCCTTTTGGCTCCC Bisulfite PCR and sequencing
Bisulfite PCR F GAGGAATTCTTTTTTTGTTYGAATATGTTG (SERPINB5 locus)
GAGGTTTTTTGGAAG Bisulfite PCR R GAGAAGCTTAAATAAAAAACRACAATACTC
(SERPINB5 locus) AACCCAACAACC pUC19 sequencing
CAGGAAACAGCTATGAC
TABLE-US-00009 TABLE 3 Sequences for primers to test sgRNA
architecture. Primers hybridize to the reverse strand of the U6
promoter unless otherwise indicated. The U6 priming site is in
bold, the guide sequence is indicated by the stretch of "N"s, the
direct repeat sequence is in italics, and the tracrRNA sequence is
underlined. The secondary structure of each sgRNA architecture is
shown in FIG. 71. primer sequence primer name (5' to 3') U6-Forward
GCCTCTAGAGGTACCTGAGGGCCTAT TTCCCATGATTCC I: sgRNA (DR + 12,
ACCTCTAGAAAAAAAGCACCGACTCG tracrRNA + 85)
GTGCCACTTTTTCAAGTTGATAACGG ACTAGCCTTATTTTAACTTGCTATTT
CTAGCTCTAAAACNNNNNNNNNNNNN NNNNNNNGGTGTTTCGTCCTTTCCAC AAG II: sgRNA
(DR + 12, ACCTCTAGAAAAAAAGCACCGACTCG tracrRNA + 85) mut2
GTGCCACTTTTTCAAGTTGATAACGG ACTAGCCTTATATTAACTTGCTATTT
CTAGCTCTAATACNNNNNNNNNNNNN NNNNNNNGGTGTTTCGTCCTTTCCAC AAG III:
sgRNA (DR + ACCTCTAGAAAAAAAGCACCGACTCG 22, tracrRNA + 85)
GTGCCACTTTTTCAAGTTGATAACGG ACTAGCCTTATTTTAACTTGCTATGC
TGTTTTGTTTCCAAAACAGCATAGCT CTAAAACNNNNNNNNNNNNNNNNNNN
NGGTGTTTCGTCCTTTCCACAAG IV: sgRNA (DR + 22,
ACCTCTAGAAAAAAAGCACCGACTCG tracrRNA + 85) mut4
GTGCCACTTTTTCAAGTTGATAACGG ACTAGCCTTATATTAACTTGCTATGC
TGTATTGTTTCCAATACAGCATAGCT CTAATACNNNNNNNNNNNNNNNNNNN
NGGTGTTTCGTCCTTTCCACAAG
TABLE-US-00010 TABLE 4 Target sites with alternate PAMs for testing
PAM specificity of Cas9. All target sites for PAM specificity
testing are found within the human EMX1 locus. Target site sequence
(5' to 3') PAM AGGCCCCAGTGGCTGCTCT NAA ACATCAACCGGTGGCGCAT NAT
AAGGTGTGGTTCCAGAACC NAC CCATCACATCAACCGGTGG NAG AAACGGCAGAAGCTGGAGG
NTA GGCAGAAGCTGGAGGAGGA NTT GGTGTGGTTCCAGAACCGG NTC
AACCGGAGGACAAAGTACA NTG TTCCAGAACCGGAGGACAA NCA GTGTGGTTCCAGAACCGGA
NCT TCCAGAACCGGAGGACAAA NCC CAGAAGCTGGAGGAGGAAG NCG
CATCAACCGGTGGCGCATT NGA GCAGAAGCTGGAGGAGGAA NGT CCTCCCTCCCTGGCCGAGG
NGC TCATCTGTGCCCCTCCCTC NAA GGGAGGACATCGATGTCAC NAT
CAAACGGCAGAAGCTGGAG NAC GGGTGGGCAACCACAAACC NAG GGTGGGCAACCACAAACCC
NTA GGCTCCCATCACATCAACC NTT GAAGGGCCTGAGTCCGAGC NTC
CAACCGGTGGCGCATTGCC NTG AGGAGGAAGGGCCTGAGTC NCA AGCTGGAGGAGGAAGGGCC
NCT GCATTGCCACGAAGCAGGC NCC ATTGCCACGAAGCAGGCCA NCG
AGAACCGGAGGACAAAGTA NGA TCAACCGGTGGCGCATTGC NGT GAAGCTGGAGGAGGAAGGG
NGC
Sequences
All sequences are in the 5' to 3' direction. For U6 transcription,
the string of underlined Ts serve as the transcriptional
terminator.
TABLE-US-00011 > U6-short tracrRNA (Streptococcus pyogenes
SF370) gagggcctatttcccatgattccttcatatttgcatatacgatacaagga
gttagagagataattggaattaatttgactgtaaacacaaagatattagt
acaaaatacgtgacgtagaaagtaataatttcttgggtagtttgcagttt
taaaattatgttttaaaatggactatcatatgcttaccgtaacttgaaag
tatttcgatttcaggctttatatatcagtggaaaggacgaaacaccGGAA
CCATTCAAAACAGCATACCAACTTAAAATAAGGCTAGTCCGTTATCAACT
TGAAAAAGTGGCACCGAGTCGGTGCTTTTTTT (tracrRNA sequence is in bold)
> U6-DR-guide sequence-DR (Streptococcus pyogenes SF370)
gagggcctatttcccatgattccttcatatttgcatatacgatacaagga
gttagagagataattggaattaatttgactgtaaacacaaagatattagt
acaaaatacgtgacgtagaaagtaataatttcttgggtagtttgcagttt
taaaattatgttttaaaatggactatcatatgcttaccgtaacttgaaag
tatttcgatttcttggctttatatatcagtggaaaggacgaaacaccggg
ttttagagctatgctgttttgaatggtcccaaaacNNNNNNNNNNNNNNN
NNNNNNNNNNNNNNNgttttagagctatgctgttttgaatggtcccaaaa cTTTTTTT (direct
repeat sequence is in italics and the guide sequence is indicated
by the stretch of "N"s) > sgRNA containing + 48 tracrRNA
(Streptococcus pyogenes SF370)
gagggcctatttcccatgattccttcatatttgcatatacgatacaaggc
tgttagagagataattggaattaatttgactgtaaacacaaagatattag
tacaaaatacgtgacgtagaaagtaataatttcttgggtagtttgcagtt
ttaaaattatgttttaaaatggactatcatatgcttaccgtaacttgaaa
gtatttcgatttcttggctttatatatcttgtggaaaggacgaaacaccN
NNNNNNNNNNNNNNNNNNNgttttagagctagaaatagcaagttaaaata
aggctagtccgTTTTTTT (guide sequence is highlighted in blue and the
tracrRNA fragment is in bold) > sgRNA containing + 54 tracrRNA
(Streptococcus pyogenes SF370)
gagggcctatacccatgattccttcatatttgcatatacgatacaaggct
gttagagagataattggaattaatttgactgtaaacacaaagatattagt
acaaaatacgtgacgtagaaagtaataatttcttgggtagtttgcagttt
taaaattatgttttaaaatggactatcatatgcttaccgtaacttgaaag
tatttcgatttcttggctttatatatcttgtggaaaggacgaaacaccNN
NNNNNNNNNNNNNNNNNNgttttagagctagaaatagcaagttaaaataa
ggctagtccgttatcaTTTTTTTT (guide sequence is indicated by the
stretch of "N"s and the tracrRNA fragment is in bold) > sgRNA
containing + 67 tracrRNA (Streptococcus pyogenes SF370)
gagggcctatttcccatgattccttcatatttgcatatacgatacaaggc
tgttagagagataattggaattaatttgactgtaaacacaaagatattag
tacaaaatacgtgacgtagaaagtaataatttcttgggtagtttgcagat
taaaattatgattaaaatggactatcatatgcttaccgtaacttgaaagt
atttcgatttcttggctttatatatcttgtggaaaggacgaaacaccNNN
NNNNNNNNNNNNNNNNNgttttagagctagaaatagcaagttaaaataag
gctagtccgttatcaactttgaaaaagtgTTTTTTT (guide sequence is indicated
by the stretch of "N"s and the tracrRNA fragment is in bold)) >
sgRNA containing + 85 tracrRNA (Streptococcus pyogenes SF370)
gagggcctatttcccatgattccttcatatttgcatatacgatacaaggc
tgttagagagataattggaattaatttgactgtaaacacaaagatattag
tacaaaatacgtgacgtagaaagtaataatttcttgggtagtttgcagtt
ttaaaattatgattaaaatggactatcatatgcttaccgtaacttgaaag
tatttcgatttcttggctttatatatcttgtggaaaggacgaaacaccNN
NNNNNNNNNNNNNNNNNNgttttagagctagaaatagcaagttaaaataa
ggctagtccgttatcaacttgaaaaagtggcaccgagtcggtgcTTTTTT T (guide
sequence is indicated by the stretch of "N"s and the tracrRNA
fragment is in bold) > CBh-NLS-SpCas9-NLS
CGTTACATAACTTACGGTAAATGGCCCGCCTGGCTGACCGCCCAACGACC
CCCGCCCATTGACGTCAATAATGACGTATGTTCCCATAGTAACGCCAATA
GGGACTTTCCATTGACGTCAATGGGTGGAGTATTTACGGTAAACTGCCCA
CTTGGCAGTACATCAAGTGTATCATATGCCAAGTACGCCCCCTATTGACG
TCAATGACGGTAAATGGCCCGCCTGGCATTATGCCCAGTACATGACCTTA
TGGGACTTTCCTACTTGGCAGTACATCTACGTATTAGTCATCGCTATTAC
CATGGTCGAGGTGAGCCCCACGTTCTGCTTCACTCTCCCCATCTCCCCCC
CCTCCCCACCCCCAATTTTGTATTTATTTATTTTTTAATTATTTTGTGCA
GCGATGGGGGCGGGGGGGGGGGGGGGGCGCGCGCCAGGCGGGGCGGGGCG
GGGCGAGGGGCGGGGCGGGGCGAGGCGGAGAGGTGCGGCGGCAGCCAATC
AGAGCGGCGCGCTCCGAAAGTTTCCTTTTATGGCGAGGCGGCGGCGGCGG
CGGCCCTATAAAAAGCGAAGCGCGCGGCGGGCGGGAGTCGCTGCGACGCT
GCCTTCGCCCCGTGCCCCGCTCCGCCGCCGCCTCGCGCCGCCCGCCCCGG
CTCTGACTGACCGCGTTACTCCCACAGGTGAGCGGGCGGGACGGCCCTTC
TCCTCCGGGCTGTAATTAGCTGAGCAAGAGGTAAGGGTTTAAGGGATGGT
TGGTTGGTGGGGTATTAATGTTTAATTACCTGGAGCACCTGCCTGAAATC
ACTTTTTTTCAGGTTGGaccggtgccaccATGGACTATAAGGACCACGAC
GGAGACTACAAGGATCATGATATTGATTACAAAGACGATGACGATAAGAT
GGCCCCAAAGAAGAAGCGGAAGGTCGGTATCCACGGAGTCCCAGCAGCCG
ACAAGAAGTACAGCATCGGCCTGGACATCGGCACCAACTCTGTGGGCTGG
GCCGTGATCACCGACGAGTACAAGGTGCCCAGCAAGAAATTCAAGGTGCT
GGGCAACACCGACCGGCACAGCATCAAGAAGAACCTGATCGGAGCCCTGC
TGTTCGACAGCGGCGAAACAGCCGAGGCCACCCGGCTGAAGAGAACCGCC
AGAAGAAGATACACCAGACGGAAGAACCGGATCTGCTATCTGCAAGAGAT
CTTCAGCAACGAGATGGCCAAGGTGGACGACAGCTTCTTCCACAGACTGG
AAGAGTCCTTCCTGGTGGAAGAGGATAAGAAGCACGAGCGGCACCCCATC
TTCGGCAACATCGTGGACGAGGTGGCCTACCACGAGAAGTACCCCACCAT
CTACCACCTGAGAAAGAAACTGGTGGACAGCACCGACAAGGCCGACCTGC
GGCTGATCTATCTGGCCCTGGCCCACATGATCAAGTTCCGGGGCCACTTC
CTGATCGAGGGCGACCTGAACCCCGACAACAGCGACGTGGACAAGCTGTT
CATCCAGCTGGTGCAGACCTACAACCAGCTGTTCGAGGAAAACCCCATCA
ACGCCAGCGGCGTGGACGCCAAGGCCATCCTGTCTGCCAGACTGAGCAAG
AGCAGACGGCTGGAAAATCTGATCGCCCAGCTGCCCGGCGAGAAGAAGAA
TGGCCTGTTCGGCAACCTGATTGCCCTGAGCCTGGGCCTGACCCCCAACT
TCAAGAGCAACTTCGACCTGGCCGAGGATGCCAAACTGCAGCTGAGCAAG
GACACCTACGACGACGACCTGGACAACCTGCTGGCCCAGATCGGCGACCA
GTACGCCGACCTCTTTCTGGCCGCCAAGAACCTGTCCGACGCCATCCTGC
TGAGCGACATCCTGAGAGTGAACACCGAGATCACCAAGGCCCCCCTGAGC
GCCTCTATGATCAAGAGATACGACGAGCACCACCAGGACCTGACCCTGCT
GAAAGCTCTCGTGCGGCAGCAGCTGCCTGAGAAGTACAAAGAGATTTTCT
TCGACCAGAGCAAGAACGGCTACGCCGGCTACATTGACGGCGGAGCCAGC
CAGGAAGAGTTCTACAAGTTCATCAAGCCCATCCTGGAAAAGATGGACGG
CACCGAGGAACTGCTCGTGAAGCTGAACAGAGAGGACCTGCTGCGGAAGC
AGCGGACCTTCGACAACGGCAGCATCCCCCACCAGATCCACCTGGGAGAG
CTGCACGCCATTCTGCGGGGGCAGGAAGATTTTTACCCATTCCTGAAGGA
CAACCGGGAAAAGATCGAGAAGATCCTGACCTTCCGCATCCCCTACTACG
TGGGCCCTCTGGCCAGGGGAAACAGCAGATTCGCCTGGATGACCAGAAAG
AGCGAGGAAACCATCACCCCCTGGAACTTCGAGGAAGTGGTGGACAAGGG
CGCTTCCGCCCAGAGCTTCATCGAGCGGATGACCAACTTCGATAAGAACC
TGCCCAACGAGAAGGTGCTGCCCAAGCACAGCCTGCTGTACGAGTACTTC
ACCGTGTATAACGAGCTGACCAAAGTGAAATAGGTGACCGAGGGAATGAG
AAAGCCCGCCTTCCTGAGCGGCGAGCAGAAAAAGGCCATCGTGGACCTGC
TGTTCAAGACCAACCGGAAAGTGACCGTGAAGCAGCTGAAAGAGGACTAC
TTCAAGAAAATCGAGTGCTTCGACTCCGTGGAAATCTCCGGCGTGGAAGA
TCGGTTCAACGCCTCCCTGGGCACATACCACGATCTGCTGAAAATTATCA
AGGACAAGGACTTCCTGGACAATGAGGAAAACGAGGACATTCTGGAAGAT
ATCGTGCTGACCCTGACACTGTTTGAGGACAGAGAGATGATCGAGGAACG
GCTGAAAACCTATGCCCACCTGTTCGACGACAAAGTGATGAAGCAGCTGA
AGCGGCGGAGATACACCGGCTGGGGCAGGCTGAGCCGGAAGCTGATCAAC
GGCATCCGGGACAAGCAGTCCGGCAAGACAATCCTGGATTTCCTGAAGTC
CGACGGCTTCGCCAACAGAAACTTCATGCAGCTGATCCACGACGACAGCC
TGACCTTTAAAGAGGACATCCAGAAAGCCCAGGTGTCCGGCCAGCGCGAT
AGCCTGCACGAGCACATTGCCAATCTGGCCGGCAGCCCCGCCATTAAGAA
GGGCATCCTGCAGACAGTGAAGGTGGTGGACGAGCTCGTGAAAGTGATGG
GCCGGCACAAGCCCGAGAACATCGTGATCGAAATGGCCAGAGAGAACCAG
ACCACCCAGAAGGGACAGAAGAACAGCCGCGAGAGAATGAAGCGGATCGA
AGAGGGCATCAAAGAGCTGGGCAGCCAGATCCTGAAAGAACACCCCGTGG
AAAACACCCAGCTGCAGAACGAGAAGCTGTACCTGTACTACCTGCAGAAT
GGGCGGGATATGTACGTGGACCAGGAACTGGACATCAACCGGCTGTCCGA
CTACGATGTGGACCATATCGTGCCTCAGAGCTTTCTGAAGGACGACTCCA
TCGACAACAAGGTGCTGACCAGAAGCGACAAGAACCGGGGCAAGAGCGAC
AACGTGCCCTCCGAACAGGTCGTGAAGAAGATGAAGAACTACTGGCGGCA
GCTGCTGAACGCCAAGCTGATTACCCAGAGAAAGTTCGACAATCTGACCA
AGGCCGAGAGAGGCGGCCTGAGCGAACTGGATAAGGCCGGCTTCATCAAG
AGACAGCTGGTGGAAACCCGGCAGATCACAAAGCACGTGGCACAGATCCT
GGACTCCCGGATGAACACTAAGTACGACGAGAATGACAAGCTGATCCGGG
AAGTGAAAGTGATCACCCTGAAGTCCAAGCTGGTGTCCGATTTCCGGAAG
GATTTCCAGTTTTACAAAGTGCGCGAGATCAACAACTACCACCACGCCCA
CGACGCCTACCTGAACGCCGTCGTGGGAACCGCCCTGATCAAAAAGTACC
CTAAGCTGGAAAGCGAGTTCGTGTACGGCGACTACAAGGTGTACGACGTG
CGGAAGATGATCGCCAAGAGCGAGCAGGAAATCGGCAAGGCTACCGCCAA
GTACTTCTTCTACAGCAACATCATGAACTTTTTCAAGACCGAGATTACCC
TGGCCAACGGCGAGATCCGGAAGCGGCCTCTGATCGAGACAAACGGCGAA
ACCGGGGAGATCGTGTGGGATAAGGGCCGGGATTTTGCCACCGTGCGGAA
AGTGCTGAGCATGCCCCAAGTGAATATCGTGAAAAAGACCGAGGTGCAGA
CAGGCGGCTTCAGCAAAGAGTCTATCCTGCCCAAGAGGAACAGCGATAAG
CTGATCGCCAGAAAGAAGGACTGGGACCCTAAGAAGTACGGCGGCTTCGA
CAGCCCCACCGTGGCCTATTCTGTGCTGGTGGTGGCCAAAGTGGAAAAGG
GCAAGTCCAAGAAACTGAAGAGTGTGAAAGAGCTGCTGGGGATCACCATC
ATGGAAAGAAGCAGCTTCGAGAAGAATCCCATCGACTTTCTGGAAGCCAA
GGGCTACAAAGAAGTGAAAAAGGACCTGATCATCAAGCTGCCTAAGTACT
CCCTGTTCCAGCTGGAAAACGGCCGGAAGAGAATGCTGGCCTCTGCCGGC
GAACTGCAGAAGGGAAACGAACTGGCCCTGCCCTCCAAATATGTGAACTT
CCTGTACCTGGCCAGCCACTATGAGAAGCTGAAGGGCTCCCCCGAGGATA
ATGAGCAGAAACAGCTGTTTGTGGAACAGCACAAGCACTACCTGGACGAG
ATCATCGAGCAGATCAGCGAGTTCTCCAAGAGAGTGATCCTGGCCGACGC
TAATCTCGACAAAGTGCTGTCCGCCTACAACAAGCACCGGGATAAGCCCA
TCAGAGAGCAGGCCGAGAATATCATCCACCTGTTTACCCTGACCAATCTG
GGAGCCCCTGCCGCCTTCAAGTACTTTGACACCACCATCGACCGGAAGAG
GTACACCAGCACCAAAGAGGTGCTGGACGCCACCCTGATCCACCAGAGCA
TCACCGGCCTGTACGAGACACGGATCGACCTGTCTCAGCTGGGAGGCGAC
TTTCTTTTTCTTAGCTTGACCAGCTTTCTTAGTAGCAGCAGGACGCTTTA A
(NLS-hSpCas9-NLS is in bold) > Sequencing amplicon for EMX1
guides 1.1, 1.14, 1.17
CCAATGGGGAGGACATCGATGTCACCTCCAATGACTAGGGTGGGCAACCA
CAAACCCACGAGGGCAGAGTGCTGCTTGCTGCTGGCCAGGCCCCTGCGTG
GGCCCAAGGTGGACTCTGGCCAC > Sequencing amplicon for EMX1 guides
1.2, 1.16 CGAGCAGAAGAAGAAGGGCTCCCATCACATCAACCGGTGGCGCATTGCCA
CGAAGCAGGCCAATGGGGAGGACATCGATGTCACCTCCAATGACTAGGGT
GGGCAACCACAAACCCACGAG > Sequencing amplicon for EMX1 guides 1.3,
1.13, 1.15 GGAGGACAAAGTACAAACGGCAGAAGCTGGAGGAGGAAGGGCCTGAGTCC
GAGCAGAAGAAGAAGGGCTCCCATCACATCAACCGGTGGCGCATTGCCAC
GAAGCAGGCCAATGGGGAGGACATCGAT > Sequencing amplicon for EMX1
guides 1.6 AGAAGCTGGAGGAGGAAGGGCCTGAGTCCGAGCAGAAGAAGAAGGGCTCC
CATCACATCAACCGGTGGCGCATTGCCACGAAGCAGGCCAATGGGGAGGA
CATCGATGTCACCTCCAATGACTAGGGTGG > Sequencing amplicon for EMX1
guides 1.10 CCTCAGTCTTCCCATCAGGCTCTCAGCTCAGCCTGAGTGTTGAGGCCCCA
GTGGCTGCTGTGGGGGCCTCCTGAGTTTCTCATCTGTGCCCCTCCCTCCC
TGGCCCAGGTGAAGGTGTGGTTCCA > Sequencing amplicon for EMX1 guides
1.11, 1.12 TCATCTGTGCCCCTCCCTCCCTGGCCCAGGTGAAGGTGTGGTTCCAGAAC
CGGAGGACAAAGTACAAACGGCAGAAGCTGGAGGAGGAAGGGCCTGAGTC
CGAGCAGAAGAAGAAGGGCTCCCATCACA > Sequencing amplicon for EMX1
guides 1.18, 1.19
CTCCAATGACTAGGGTGGGCAACCACAAACCCACGAGGGCAGAGTGCTGC
TTGCTGCTGGCCAGGCCCCTGCGTGGGCCCAAGCTGGACTCTGGCCACTC
CCTGGCCAGGCTTTGGGGAGGCCTGGAGT > Sequencing amplicon for EMX1
guides 1.20 CTGCTTGCTGCTGGCCAGGCCCCTGCGTGGGCCCAAGCTGGACTCTGGCC
ACTCCCTGGCCAGGCTTTGGGGAGGCCTGGAGTCATGGCCCCACAGGGCT
TGAAGCCCGGGGCCGCCATTGACAGAG > T7 promoter F primer for annealing
with target strand GAAATTAATACGACTCACTATAGGG > oligo containing
pUC19 target site 1 for methylation (T7 reverse)
AAAAAAGCACCGACTCGGTGCCACTTTTTCAAGTTGATAACGGACTAGCC
TTATTTTAACTTTGCTATTTCTAGCTCTAAAACAACGACGAGCGTGACAC
CACCCTATAGTGAGTCGTATTAATTTC > oligo containing pUC19 target site
2 for methylation (T7 reverse)
AAAAAAGCACCGACTCGGTGCCACTTTTTCAAGTTGATAACGGACTAGCC
TTATTTTAACTTGCTATTTCTAGCTCTAAAACGCAACAATTAATAGACTG
GACCTATAGTGAGTCGTATTAATTTC
Example 7
Base Pair Mismatching Investigations
Applicants have tested several mismatch guide sequences have been
tested to probe cas9 protein off-target effect (Hsu et al., DNA
targeting specificity of RNA-guided Cas9 nucleases. Nat Biotechnol.
2013 September; 31(9):827-32). However the number of mismatch guide
sequences used may have been far less than the complete set of all
possible mismatch guide sequences. In order to predict off target
cutting efficiency of non-tested guide-target pairs, a
thermodynamic model is built to fit existing experimental data and
is used to predict new guide target pair.
Applicants tested cutting frequency data for tested guide target
pair collected from Hsu P. D et al., 2013. Applicants collected the
nearest neighbor thermodynamic parameters for nucleic acid pairing
from ViennaRNA package distribution. Applicants obtained first
bi-base pair thermal dynamical parameters per position for each
guide target pair. These parameters were referred to as features.
Data, comprising of cutting frequency and features for each guide
target pair, was divided into 5 groups randomly in order to perform
5 fold cross validation. In each training session, 4 groups of data
were used as training data. A modified radial basis kernel that
consisted of radial basis kernel with an additive term accounting
for different PAM was used in nonlinear regression. In each testing
session, the remaining 1 group was used to test the model. Coarse
grid search for fitting parameters was performed. The best fitting
parameters were used to generate new parameters in fine grid
search.
The Applicants' algorithm flows as follows: 1. Choose data with
duplicate. 2. Calculate confidence interval for cutting frequency
based on binomial distribution. Select data with p value passing a
threshold. 3. Calculate thermal dynamic parameters for each
guide-target pair to obtain its feature. 4. Transform non-linearly
cutting frequency data in order to balance high and low cutting
frequency contributions in model fitting. 5. Divide data into 5
groups. 6. Perform coarse grid search for fitting parameter using 5
fold cross validation. 7. Best parameters are selected, and fine
grid search is performed.
Result: Training data and testing data are shown as blue dots and
red dots respectively in the scatter plot. Spearman correlation
coefficient is 0.88, and Pearson correlation coefficient is 0.91
(FIG. 36). It demonstrates good model prediction of the data. In
order to check model over fitting, data was randomized again,
trained and tested using the same parameters. The result is 0.82
for both correlation coefficients (FIG. 37).
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While preferred embodiments of the present invention have been
shown and described herein, it will be obvious to those skilled in
the art that such embodiments are provided by way of example only.
Numerous variations, changes, and substitutions will now occur to
those skilled in the art without departing from the invention. It
should be understood that various alternatives to the embodiments
of the invention described herein may be employed in practicing the
invention.
SEQUENCE LISTINGS
1
1076112RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 1guuuuagagc ua 1227PRTSimian
virus 40 2Pro Lys Lys Lys Arg Lys Val1
5316PRTUnknownsource/note="Description of Unknown Nucleoplasmin
bipartite NLS sequence" 3Lys Arg Pro Ala Ala Thr Lys Lys Ala Gly
Gln Ala Lys Lys Lys Lys1 5 10
1549PRTUnknownsource/note="Description of Unknown C-myc NLS
sequence" 4Pro Ala Ala Lys Arg Val Lys Leu Asp1
5511PRTUnknownsource/note="Description of Unknown C-myc NLS
sequence" 5Arg Gln Arg Arg Asn Glu Leu Lys Arg Ser Pro1 5
10638PRTHomo sapiens 6Asn Gln Ser Ser Asn Phe Gly Pro Met Lys Gly
Gly Asn Phe Gly Gly1 5 10 15Arg Ser Ser Gly Pro Tyr Gly Gly Gly Gly
Gln Tyr Phe Ala Lys Pro 20 25 30Arg Asn Gln Gly Gly Tyr
35742PRTUnknownsource/note="Description of Unknown IBB domain from
importin-alpha sequence" 7Arg Met Arg Ile Glx Phe Lys Asn Lys Gly
Lys Asp Thr Ala Glu Leu1 5 10 15Arg Arg Arg Arg Val Glu Val Ser Val
Glu Leu Arg Lys Ala Lys Lys 20 25 30Asp Glu Gln Ile Leu Lys Arg Arg
Asn Val 35 4088PRTUnknownsource/note="Description of Unknown Myoma
T protein sequence" 8Val Ser Arg Lys Arg Pro Arg Pro1
598PRTUnknownsource/note="Description of Unknown Myoma T protein
sequence" 9Pro Pro Lys Lys Ala Arg Glu Asp1 5108PRTHomo sapiens
10Pro Gln Pro Lys Lys Lys Pro Leu1 51112PRTMus musculus 11Ser Ala
Leu Ile Lys Lys Lys Lys Lys Met Ala Pro1 5 10125PRTInfluenza virus
12Asp Arg Leu Arg Arg1 5137PRTInfluenza virus 13Pro Lys Gln Lys Lys
Arg Lys1 51410PRTHepatitus delta virus 14Arg Lys Leu Lys Lys Lys
Ile Lys Lys Leu1 5 101510PRTMus musculus 15Arg Glu Lys Lys Lys Phe
Leu Lys Arg Arg1 5 101620PRTHomo sapiens 16Lys Arg Lys Gly Asp Glu
Val Asp Gly Val Asp Glu Val Ala Lys Lys1 5 10 15Lys Ser Lys Lys
201717PRTHomo sapiens 17Arg Lys Cys Leu Gln Ala Gly Met Asn Leu Glu
Ala Arg Lys Thr Lys1 5 10 15Lys1827DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"modified_base(1)..(20)a, c, t or
gmodified_base(21)..(22)a, c, t, g, unknown or other 18nnnnnnnnnn
nnnnnnnnnn nnagaaw 271919DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"modified_base(1)..(12)a, c, t or
gmodified_base(13)..(14)a, c, t, g, unknown or other 19nnnnnnnnnn
nnnnagaaw 192027DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic
oligonucleotide"modified_base(1)..(20)a, c, t or
gmodified_base(21)..(22)a, c, t, g, unknown or other 20nnnnnnnnnn
nnnnnnnnnn nnagaaw 272118DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"modified_base(1)..(11)a, c, t or
gmodified_base(12)..(13)a, c, t, g, unknown or other 21nnnnnnnnnn
nnnagaaw 1822137DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic
polynucleotide"modified_base(1)..(20)a, c, t, g, unknown or other
22nnnnnnnnnn nnnnnnnnnn gtttttgtac tctcaagatt tagaaataaa tcttgcagaa
60gctacaaaga taaggcttca tgccgaaatc aacaccctgt cattttatgg cagggtgttt
120tcgttattta atttttt 13723123DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
polynucleotide"modified_base(1)..(20)a, c, t, g, unknown or other
23nnnnnnnnnn nnnnnnnnnn gtttttgtac tctcagaaat gcagaagcta caaagataag
60gcttcatgcc gaaatcaaca ccctgtcatt ttatggcagg gtgttttcgt tatttaattt
120ttt 12324110DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic
polynucleotide"modified_base(1)..(20)a, c, t, g, unknown or other
24nnnnnnnnnn nnnnnnnnnn gtttttgtac tctcagaaat gcagaagcta caaagataag
60gcttcatgcc gaaatcaaca ccctgtcatt ttatggcagg gtgttttttt
11025102DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic
polynucleotide"modified_base(1)..(20)a, c, t, g, unknown or other
25nnnnnnnnnn nnnnnnnnnn gttttagagc tagaaatagc aagttaaaat aaggctagtc
60cgttatcaac ttgaaaaagt ggcaccgagt cggtgctttt tt
1022688DNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide"modified_base(1)..(20)a, c, t,
g, unknown or other 26nnnnnnnnnn nnnnnnnnnn gttttagagc tagaaatagc
aagttaaaat aaggctagtc 60cgttatcaac ttgaaaaagt gttttttt
882776DNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide"modified_base(1)..(20)a, c, t,
g, unknown or other 27nnnnnnnnnn nnnnnnnnnn gttttagagc tagaaatagc
aagttaaaat aaggctagtc 60cgttatcatt tttttt 762822DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
28aggccccagt ggctgctctn aa 222922DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
29acatcaaccg gtggcgcatn at 223022DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
30aaggtgtggt tccagaaccn ac 223122DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
31ccatcacatc aaccggtggn ag 223222DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
32aaacggcaga agctggaggn ta 223322DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
33ggcagaagct ggaggaggan tt 223422DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
34ggtgtggttc cagaaccggn tc 223522DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
35aaccggagga caaagtacan tg 223622DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
36ttccagaacc ggaggacaan ca 223722DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
37gtgtggttcc agaaccggan ct 223822DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
38tccagaaccg gaggacaaan cc 223922DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
39cagaagctgg aggaggaagn cg 224022DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
40catcaaccgg tggcgcattn ga 224122DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
41gcagaagctg gaggaggaan gt 224222DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
42cctccctccc tggcccaggn gc 224322DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
43tcatctgtgc ccctccctcn aa 224422DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
44gggaggacat cgatgtcacn at 224522DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
45caaacggcag aagctggagn ac 224622DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
46gggtgggcaa ccacaaaccn ag 224722DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
47ggtgggcaac cacaaacccn ta 224822DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
48ggctcccatc acatcaaccn tt 224922DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
49gaagggcctg agtccgagcn tc 225022DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
50caaccggtgg cgcattgccn tg 225122DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
51aggaggaagg gcctgagtcn ca 225222DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
52agctggagga ggaagggccn ct 225322DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
53gcattgccac gaagcaggcn cc 225422DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
54attgccacga agcaggccan cg 225522DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
55agaaccggag gacaaagtan ga 225622DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
56tcaaccggtg gcgcattgcn gt 225722DNAHomo
sapiensmodified_base(20)..(20)a, c, t, g, unknown or other
57gaagctggag gaggaagggn gc 225823DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 58gccaaattgg acgaccctcg cgg 235923DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 59cgaggagacc cccgtttcgg tgg 236023DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 60cccgccgccg ccgtggctcg agg 236123DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 61tgagctctac gagatccaca agg 236223DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 62ctcaaaattc ataccggttg tgg 236323DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 63cgttaaacaa caaccggact tgg 236423DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 64ttcaccccgc ggcgctgaat ggg 236523DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 65accactacca gtccgtccac agg 236623DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 66cactgcttaa gcctcgctcg agg 236723DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 67tcaccagcaa tattcgctcg agg 236823DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 68caccagcaat attccgctcg agg 236923DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 69tagcaacaga catacgctcg agg 237023DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 70gggcagtagt aatacgctcg agg 237123DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 71ccaattccca tacattattg tac 237233DNAHomo sapiens
72ggacatcgat gtcacctcca atgactaggg tgg 337333DNAHomo sapiens
73cattggaggt gacatcgatg tcctccccat tgg 337433DNAHomo sapiens
74ggaagggcct gagtccgagc agaagaagaa ggg 337533DNAHomo sapiens
75ggtggcgaga ggggccgaga ttgggtgttc agg 337633DNAHomo sapiens
76atgcaggagg gtggcgagag gggccgagat tgg 337723DNAHomo sapiens
77gtcacctcca atgactaggg tgg 237823DNAHomo sapiens 78gacatcgatg
tcctccccat tgg 237923DNAHomo sapiens 79gagtccgagc agaagaagaa ggg
238023DNAHomo sapiens 80gcgccaccgg ttgatgtgat ggg 238123DNAHomo
sapiens 81ggggcacaga tgagaaactc agg 238223DNAHomo sapiens
82gtacaaacgg cagaagctgg agg 238323DNAHomo sapiens 83ggcagaagct
ggaggaggaa ggg 238423DNAHomo sapiens 84ggagcccttc ttcttctgct cgg
238523DNAHomo sapiens 85gggcaaccac aaacccacga ggg 238623DNAHomo
sapiens 86gctcccatca catcaaccgg tgg 238723DNAHomo sapiens
87gtggcgcatt gccacgaagc agg 238823DNAHomo sapiens 88ggcagagtgc
tgcttgctgc tgg 238923DNAHomo sapiens 89gcccctgcgt gggcccaagc tgg
239023DNAHomo sapiens 90gagtggccag agtccagctt ggg 239123DNAHomo
sapiens 91ggcctcccca aagcctggcc agg 239223DNAHomo sapiens
92ggggccgaga ttgggtgttc agg 239323DNAHomo sapiens 93gtggcgagag
gggccgagat tgg 239423DNAHomo sapiens 94gagtgccgcc gaggcggggc ggg
239523DNAHomo sapiens 95ggagtgccgc cgaggcgggg cgg 239623DNAHomo
sapiens 96ggagaggagt gccgccgagg cgg 239721DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
primer" 97aaaaccaccc ttctctctgg c 219821DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
primer" 98ggagattgga gacacggaga g 219920DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
primer" 99ccatcccctt ctgtgaatgt 2010020DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
primer" 100ggagattgga gacacggaga 2010120DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
primer" 101ctggaaagcc aatgcctgac 2010220DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
primer" 102ggcagcaaac tccttgtcct 2010320DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
primer" 103aagcaccgac tcggtgccac 2010420DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
primer" 104tcacctccaa tgactagggg 2010522DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
primer" 105caagttgata acggactagc ct 2210623DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
primer" 106agtccgagca gaagaagaag ttt 2310725DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
primer" 107tttcaagttg ataacggact agcct 2510820DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
primer" 108aaacagcaga ttcgcctgga 2010920DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
primer" 109tcatccgctc gatgaagctc 2011020DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
primer" 110tccaaaatca agtggggcga 2011120DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
primer" 111tgatgaccct tttggctccc 2011245DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
primer" 112gaggaattct ttttttgtty gaatatgttg gaggtttttt ggaag
4511342DNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic primer" 113gagaagctta aataaaaaac racaatactc
aacccaacaa cc 4211417DNAArtificial Sequencesource/note="Description
of Artificial Sequence Synthetic primer" 114caggaaacag ctatgac
1711539DNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic primer" 115gcctctagag gtacctgagg gcctatttcc
catgattcc 39116133DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic primer"modified_base(92)..(111)a, c,
t, g, unknown or other 116acctctagaa aaaaagcacc gactcggtgc
cactttttca agttgataac ggactagcct 60tattttaact tgctatttct agctctaaaa
cnnnnnnnnn nnnnnnnnnn nggtgtttcg 120tcctttccac aag
133117133DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic primer"modified_base(92)..(111)a, c,
t, g, unknown or other 117acctctagaa aaaaagcacc gactcggtgc
cactttttca agttgataac ggactagcct 60tatattaact tgctatttct agctctaata
cnnnnnnnnn nnnnnnnnnn nggtgtttcg 120tcctttccac aag
133118153DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic primer"modified_base(112)..(131)a, c,
t, g, unknown or other 118acctctagaa aaaaagcacc gactcggtgc
cactttttca agttgataac ggactagcct 60tattttaact tgctatgctg ttttgtttcc
aaaacagcat agctctaaaa cnnnnnnnnn 120nnnnnnnnnn nggtgtttcg
tcctttccac aag 153119153DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
primer"modified_base(112)..(131)a, c, t, g, unknown or other
119acctctagaa aaaaagcacc gactcggtgc cactttttca agttgataac
ggactagcct 60tatattaact tgctatgctg tattgtttcc aatacagcat agctctaata
cnnnnnnnnn 120nnnnnnnnnn nggtgtttcg tcctttccac aag
153120335DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic polynucleotide" 120gagggcctat
ttcccatgat tccttcatat ttgcatatac gatacaaggc tgttagagag 60ataattggaa
ttaatttgac tgtaaacaca aagatattag tacaaaatac gtgacgtaga
120aagtaataat ttcttgggta gtttgcagtt ttaaaattat gttttaaaat
ggactatcat 180atgcttaccg taacttgaaa gtatttcgat ttcttggctt
tatatatctt gtggaaagga 240cgaaacaccg gaaccattca aaacagcata
gcaagttaaa ataaggctag tccgttatca 300acttgaaaaa gtggcaccga
gtcggtgctt ttttt 335121360DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
polynucleotide"modified_base(288)..(317)a, c, t, g, unknown or
other 121gagggcctat ttcccatgat tccttcatat ttgcatatac gatacaaggc
tgttagagag 60ataattggaa ttaatttgac tgtaaacaca aagatattag tacaaaatac
gtgacgtaga 120aagtaataat ttcttgggta gtttgcagtt ttaaaattat
gttttaaaat ggactatcat 180atgcttaccg taacttgaaa gtatttcgat
ttcttggctt tatatatctt gtggaaagga 240cgaaacaccg ggttttagag
ctatgctgtt ttgaatggtc ccaaaacnnn nnnnnnnnnn 300nnnnnnnnnn
nnnnnnngtt ttagagctat gctgttttga atggtcccaa aacttttttt
360122318DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic
polynucleotide"modified_base(250)..(269)a, c, t, g, unknown or
other 122gagggcctat ttcccatgat tccttcatat ttgcatatac gatacaaggc
tgttagagag 60ataattggaa ttaatttgac tgtaaacaca aagatattag tacaaaatac
gtgacgtaga 120aagtaataat ttcttgggta gtttgcagtt ttaaaattat
gttttaaaat ggactatcat 180atgcttaccg taacttgaaa gtatttcgat
ttcttggctt tatatatctt gtggaaagga 240cgaaacaccn nnnnnnnnnn
nnnnnnnnng ttttagagct agaaatagca agttaaaata 300aggctagtcc gttttttt
318123325DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic
polynucleotide"modified_base(250)..(269)a, c, t, g, unknown or
other 123gagggcctat ttcccatgat tccttcatat ttgcatatac gatacaaggc
tgttagagag 60ataattggaa ttaatttgac tgtaaacaca aagatattag tacaaaatac
gtgacgtaga 120aagtaataat ttcttgggta gtttgcagtt ttaaaattat
gttttaaaat ggactatcat 180atgcttaccg taacttgaaa gtatttcgat
ttcttggctt tatatatctt gtggaaagga 240cgaaacaccn nnnnnnnnnn
nnnnnnnnng ttttagagct agaaatagca agttaaaata 300aggctagtcc
gttatcattt ttttt 325124337DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
polynucleotide"modified_base(250)..(269)a, c, t, g, unknown or
other 124gagggcctat ttcccatgat tccttcatat ttgcatatac gatacaaggc
tgttagagag 60ataattggaa ttaatttgac tgtaaacaca aagatattag tacaaaatac
gtgacgtaga 120aagtaataat ttcttgggta gtttgcagtt ttaaaattat
gttttaaaat ggactatcat 180atgcttaccg taacttgaaa gtatttcgat
ttcttggctt tatatatctt gtggaaagga 240cgaaacaccn nnnnnnnnnn
nnnnnnnnng ttttagagct agaaatagca agttaaaata 300aggctagtcc
gttatcaact tgaaaaagtg ttttttt 337125352DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
polynucleotide"modified_base(250)..(269)a, c, t, g, unknown or
other 125gagggcctat ttcccatgat tccttcatat ttgcatatac gatacaaggc
tgttagagag 60ataattggaa ttaatttgac tgtaaacaca aagatattag tacaaaatac
gtgacgtaga 120aagtaataat ttcttgggta gtttgcagtt ttaaaattat
gttttaaaat ggactatcat 180atgcttaccg taacttgaaa gtatttcgat
ttcttggctt tatatatctt gtggaaagga 240cgaaacaccn nnnnnnnnnn
nnnnnnnnng ttttagagct agaaatagca agttaaaata 300aggctagtcc
gttatcaact tgaaaaagtg gcaccgagtc ggtgcttttt tt
3521265101DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic polynucleotide" 126cgttacataa
cttacggtaa atggcccgcc tggctgaccg cccaacgacc cccgcccatt 60gacgtcaata
atgacgtatg ttcccatagt aacgccaata gggactttcc attgacgtca
120atgggtggag tatttacggt aaactgccca cttggcagta catcaagtgt
atcatatgcc 180aagtacgccc cctattgacg tcaatgacgg taaatggccc
gcctggcatt atgcccagta 240catgacctta tgggactttc ctacttggca
gtacatctac gtattagtca tcgctattac 300catggtcgag gtgagcccca
cgttctgctt cactctcccc atctcccccc cctccccacc 360cccaattttg
tatttattta ttttttaatt attttgtgca gcgatggggg cggggggggg
420gggggggcgc gcgccaggcg gggcggggcg gggcgagggg cggggcgggg
cgaggcggag 480aggtgcggcg gcagccaatc agagcggcgc gctccgaaag
tttcctttta tggcgaggcg 540gcggcggcgg cggccctata aaaagcgaag
cgcgcggcgg gcgggagtcg ctgcgacgct 600gccttcgccc cgtgccccgc
tccgccgccg cctcgcgccg cccgccccgg ctctgactga 660ccgcgttact
cccacaggtg agcgggcggg acggcccttc tcctccgggc tgtaattagc
720tgagcaagag gtaagggttt aagggatggt tggttggtgg ggtattaatg
tttaattacc 780tggagcacct gcctgaaatc actttttttc aggttggacc
ggtgccacca tggactataa 840ggaccacgac ggagactaca aggatcatga
tattgattac aaagacgatg acgataagat 900ggccccaaag aagaagcgga
aggtcggtat ccacggagtc ccagcagccg acaagaagta 960cagcatcggc
ctggacatcg gcaccaactc tgtgggctgg gccgtgatca ccgacgagta
1020caaggtgccc agcaagaaat tcaaggtgct gggcaacacc gaccggcaca
gcatcaagaa 1080gaacctgatc ggagccctgc tgttcgacag cggcgaaaca
gccgaggcca cccggctgaa 1140gagaaccgcc agaagaagat acaccagacg
gaagaaccgg atctgctatc tgcaagagat 1200cttcagcaac gagatggcca
aggtggacga cagcttcttc cacagactgg aagagtcctt 1260cctggtggaa
gaggataaga agcacgagcg gcaccccatc ttcggcaaca tcgtggacga
1320ggtggcctac cacgagaagt accccaccat ctaccacctg agaaagaaac
tggtggacag 1380caccgacaag gccgacctgc ggctgatcta tctggccctg
gcccacatga tcaagttccg 1440gggccacttc ctgatcgagg gcgacctgaa
ccccgacaac agcgacgtgg acaagctgtt 1500catccagctg gtgcagacct
acaaccagct gttcgaggaa aaccccatca acgccagcgg 1560cgtggacgcc
aaggccatcc tgtctgccag actgagcaag agcagacggc tggaaaatct
1620gatcgcccag ctgcccggcg agaagaagaa tggcctgttc ggcaacctga
ttgccctgag 1680cctgggcctg acccccaact tcaagagcaa cttcgacctg
gccgaggatg ccaaactgca 1740gctgagcaag gacacctacg acgacgacct
ggacaacctg ctggcccaga tcggcgacca 1800gtacgccgac ctgtttctgg
ccgccaagaa cctgtccgac gccatcctgc tgagcgacat 1860cctgagagtg
aacaccgaga tcaccaaggc ccccctgagc gcctctatga tcaagagata
1920cgacgagcac caccaggacc tgaccctgct gaaagctctc gtgcggcagc
agctgcctga 1980gaagtacaaa gagattttct tcgaccagag caagaacggc
tacgccggct acattgacgg 2040cggagccagc caggaagagt tctacaagtt
catcaagccc atcctggaaa agatggacgg 2100caccgaggaa ctgctcgtga
agctgaacag agaggacctg ctgcggaagc agcggacctt 2160cgacaacggc
agcatccccc accagatcca cctgggagag ctgcacgcca ttctgcggcg
2220gcaggaagat ttttacccat tcctgaagga caaccgggaa aagatcgaga
agatcctgac 2280cttccgcatc ccctactacg tgggccctct ggccagggga
aacagcagat tcgcctggat 2340gaccagaaag agcgaggaaa ccatcacccc
ctggaacttc gaggaagtgg tggacaaggg 2400cgcttccgcc cagagcttca
tcgagcggat gaccaacttc gataagaacc tgcccaacga 2460gaaggtgctg
cccaagcaca gcctgctgta cgagtacttc accgtgtata acgagctgac
2520caaagtgaaa tacgtgaccg agggaatgag aaagcccgcc ttcctgagcg
gcgagcagaa 2580aaaggccatc gtggacctgc tgttcaagac caaccggaaa
gtgaccgtga agcagctgaa 2640agaggactac ttcaagaaaa tcgagtgctt
cgactccgtg gaaatctccg gcgtggaaga 2700tcggttcaac gcctccctgg
gcacatacca cgatctgctg aaaattatca aggacaagga 2760cttcctggac
aatgaggaaa acgaggacat tctggaagat atcgtgctga ccctgacact
2820gtttgaggac agagagatga tcgaggaacg gctgaaaacc tatgcccacc
tgttcgacga 2880caaagtgatg aagcagctga agcggcggag atacaccggc
tggggcaggc tgagccggaa 2940gctgatcaac ggcatccggg acaagcagtc
cggcaagaca atcctggatt tcctgaagtc 3000cgacggcttc gccaacagaa
acttcatgca gctgatccac gacgacagcc tgacctttaa 3060agaggacatc
cagaaagccc aggtgtccgg ccagggcgat agcctgcacg agcacattgc
3120caatctggcc ggcagccccg ccattaagaa gggcatcctg cagacagtga
aggtggtgga 3180cgagctcgtg aaagtgatgg gccggcacaa gcccgagaac
atcgtgatcg aaatggccag 3240agagaaccag accacccaga agggacagaa
gaacagccgc gagagaatga agcggatcga 3300agagggcatc aaagagctgg
gcagccagat cctgaaagaa caccccgtgg aaaacaccca 3360gctgcagaac
gagaagctgt acctgtacta cctgcagaat gggcgggata tgtacgtgga
3420ccaggaactg gacatcaacc ggctgtccga ctacgatgtg gaccatatcg
tgcctcagag 3480ctttctgaag gacgactcca tcgacaacaa ggtgctgacc
agaagcgaca agaaccgggg 3540caagagcgac aacgtgccct ccgaagaggt
cgtgaagaag atgaagaact actggcggca 3600gctgctgaac gccaagctga
ttacccagag aaagttcgac aatctgacca aggccgagag 3660aggcggcctg
agcgaactgg ataaggccgg cttcatcaag agacagctgg tggaaacccg
3720gcagatcaca aagcacgtgg cacagatcct ggactcccgg atgaacacta
agtacgacga 3780gaatgacaag ctgatccggg aagtgaaagt gatcaccctg
aagtccaagc tggtgtccga 3840tttccggaag gatttccagt tttacaaagt
gcgcgagatc aacaactacc accacgccca 3900cgacgcctac ctgaacgccg
tcgtgggaac cgccctgatc aaaaagtacc ctaagctgga 3960aagcgagttc
gtgtacggcg actacaaggt gtacgacgtg cggaagatga tcgccaagag
4020cgagcaggaa atcggcaagg ctaccgccaa gtacttcttc tacagcaaca
tcatgaactt 4080tttcaagacc gagattaccc tggccaacgg cgagatccgg
aagcggcctc tgatcgagac 4140aaacggcgaa accggggaga tcgtgtggga
taagggccgg gattttgcca ccgtgcggaa 4200agtgctgagc atgccccaag
tgaatatcgt gaaaaagacc gaggtgcaga caggcggctt 4260cagcaaagag
tctatcctgc ccaagaggaa cagcgataag ctgatcgcca gaaagaagga
4320ctgggaccct aagaagtacg gcggcttcga cagccccacc gtggcctatt
ctgtgctggt 4380ggtggccaaa gtggaaaagg gcaagtccaa gaaactgaag
agtgtgaaag agctgctggg 4440gatcaccatc atggaaagaa gcagcttcga
gaagaatccc atcgactttc tggaagccaa 4500gggctacaaa gaagtgaaaa
aggacctgat catcaagctg cctaagtact ccctgttcga 4560gctggaaaac
ggccggaaga gaatgctggc ctctgccggc gaactgcaga agggaaacga
4620actggccctg ccctccaaat atgtgaactt cctgtacctg gccagccact
atgagaagct 4680gaagggctcc cccgaggata atgagcagaa acagctgttt
gtggaacagc acaagcacta 4740cctggacgag atcatcgagc agatcagcga
gttctccaag agagtgatcc tggccgacgc 4800taatctggac aaagtgctgt
ccgcctacaa caagcaccgg gataagccca tcagagagca 4860ggccgagaat
atcatccacc tgtttaccct gaccaatctg ggagcccctg ccgccttcaa
4920gtactttgac accaccatcg accggaagag gtacaccagc accaaagagg
tgctggacgc 4980caccctgatc caccagagca tcaccggcct gtacgagaca
cggatcgacc tgtctcagct 5040gggaggcgac tttctttttc ttagcttgac
cagctttctt agtagcagca ggacgcttta 5100a 5101127123DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
polynucleotide" 127ccaatgggga ggacatcgat gtcacctcca atgactaggg
tgggcaacca caaacccacg 60agggcagagt gctgcttgct gctggccagg cccctgcgtg
ggcccaagct ggactctggc 120cac 123128121DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
polynucleotide" 128cgagcagaag aagaagggct cccatcacat caaccggtgg
cgcattgcca cgaagcaggc 60caatggggag gacatcgatg tcacctccaa tgactagggt
gggcaaccac aaacccacga 120g 121129128DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
polynucleotide" 129ggaggacaaa gtacaaacgg cagaagctgg aggaggaagg
gcctgagtcc gagcagaaga 60agaagggctc ccatcacatc aaccggtggc gcattgccac
gaagcaggcc aatggggagg 120acatcgat 128130130DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
polynucleotide" 130agaagctgga ggaggaaggg cctgagtccg agcagaagaa
gaagggctcc catcacatca 60accggtggcg cattgccacg aagcaggcca atggggagga
catcgatgtc acctccaatg 120actagggtgg 130131125DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
polynucleotide" 131cctcagtctt cccatcaggc tctcagctca gcctgagtgt
tgaggcccca gtggctgctc 60tgggggcctc ctgagtttct catctgtgcc cctccctccc
tggcccaggt gaaggtgtgg 120ttcca 125132129DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
polynucleotide" 132tcatctgtgc ccctccctcc ctggcccagg tgaaggtgtg
gttccagaac cggaggacaa 60agtacaaacg gcagaagctg gaggaggaag ggcctgagtc
cgagcagaag aagaagggct 120cccatcaca 129133129DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
polynucleotide" 133ctccaatgac tagggtgggc aaccacaaac ccacgagggc
agagtgctgc ttgctgctgg 60ccaggcccct gcgtgggccc aagctggact ctggccactc
cctggccagg ctttggggag 120gcctggagt 129134127DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
polynucleotide" 134ctgcttgctg ctggccaggc ccctgcgtgg gcccaagctg
gactctggcc actccctggc 60caggctttgg ggaggcctgg agtcatggcc ccacagggct
tgaagcccgg ggccgccatt 120gacagag 12713525DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
primer" 135gaaattaata cgactcacta taggg 25136126DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
polynucleotide" 136aaaaaagcac cgactcggtg ccactttttc aagttgataa
cggactagcc ttattttaac 60ttgctatttc tagctctaaa acaacgacga gcgtgacacc
accctatagt gagtcgtatt 120aatttc 126137126DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
polynucleotide" 137aaaaaagcac cgactcggtg ccactttttc aagttgataa
cggactagcc ttattttaac 60ttgctatttc tagctctaaa acgcaacaat taatagactg
gacctatagt gagtcgtatt 120aatttc 126138102DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
polynucleotide" 138gttttagagc tatgctgttt tgaatggtcc caaaacggaa
gggcctgagt ccgagcagaa 60gaagaagttt tagagctatg ctgttttgaa tggtcccaaa
ac 102139100DNAHomo sapiens 139cggaggacaa agtacaaacg gcagaagctg
gaggaggaag ggcctgagtc cgagcagaag 60aagaagggct cccatcacat caaccggtgg
cgcattgcca 10014050DNAHomo sapiens 140agctggagga ggaagggcct
gagtccgagc agaagaagaa gggctcccac 5014130RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 141gaguccgagc agaagaagaa guuuuagagc 3014249DNAHomo
sapiens 142agctggagga ggaagggcct gagtccgagc agaagagaag ggctcccat
4914353DNAHomo sapiens 143ctggaggagg aagggcctga gtccgagcag
aagaagaagg gctcccatca cat 5314452DNAHomo sapiens 144ctggaggagg
aagggcctga gtccgagcag aagagaaggg ctcccatcac at 5214554DNAHomo
sapiens 145ctggaggagg aagggcctga gtccgagcag aagaaagaag ggctcccatc
acat 5414650DNAHomo sapiens 146ctggaggagg aagggcctga gtccgagcag
aagaagggct cccatcacat 5014747DNAHomo sapiens 147ctggaggagg
aagggcctga gcccgagcag aagggctccc atcacat 4714823DNAHomo sapiens
148gtcacctcca atgactaggg tgg 2314923DNAHomo sapiens 149gcgccaccgg
ttgatgtgat ggg 2315099RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"modified_base(1)..(20)a, c, u, g, unknown or other
150nnnnnnnnnn nnnnnnnnnn guuuuagagc uagaaauagc aaguuaaaau
aaggcuaguc 60cguuaucaac uugaaaaagu ggcaccgagu cggugcuuu
9915164DNAHomo sapiens 151caagaggctt gagtaggaga ggagtgccgc
cgaggcgggg cggggcgggg cgtggagctg 60ggct 6415220RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 152gucaccucca augacuaggg 2015320RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 153gucaccucca augacuaaga 2015420RNAArtificial
Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 154gucaccucca augauuagga
2015520RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 155gucaccucca gugacuagga
2015620RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 156gucacuucca augacuagga
2015720RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 157aucaccucca augacuagga
2015820RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 158gucaccucca augaccaagg
2015920RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 159gucaccucca auaacuaagg
2016020RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 160gucaccucua augacuaagg
2016120RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 161gucgccucca augacuaagg
2016220RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 162gucaccucca auggccaggg
2016320RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 163gucaccucca gugaccaggg
2016420RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 164gucaccccca augaccaggg
2016520RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 165gccaccucca augaccaggg
2016620RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 166gucaccucca augaccaaga
2016720RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 167gucaccucca auggcuggga
2016820RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 168gucaccucca acgaccagga
2016920RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 169gucaccuccg augauuagga
2017020RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 170gucaccucca auggccaagg
2017120RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 171gucaccucca acgauuaagg
2017220RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 172gucaccuccg auggcuaagg
2017320RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 173gucaccuuca auaacuaagg
2017420RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 174gucaccucca auggccaaga
2017520RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 175gucaccucca guggcuggga
2017620RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 176gucaccuuca acgaccagga
2017720RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 177gucaucuccg augauuagga
2017820RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 178gccaccuuca auggcuagga
2017920RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 179gucaccucca augacuagaa
2018020RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 180gucaccucca augacugagg
2018120RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 181gucaccucca augaucaggg
2018220RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 182gucaccucca auagcuaggg
2018320RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 183gucaccucca gcgacuaggg
2018420RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 184gucaccucug augacuaggg
2018520RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 185gucacccuca augacuaggg
2018620RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 186gucauuucca augacuaggg
2018720RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 187guugccucca augacuaggg
2018820RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 188accaccucca augacuaggg
2018920RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 189gucaccucca augacugaag
2019020RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 190gucaccucca auggucaggg
2019120RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 191gucaccucca gcaacuaggg
2019220RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 192gucaccuuug augacuaggg
2019320RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 193gucauuccca augacuaggg
2019420RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 194gcugccucca augacuaggg
2019520RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 195gucaccucca augaccgaaa
2019620RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 196gucaccucca auagucgggg
2019720RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 197gucaccuccg gcagcuaggg
2019820RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 198gucacccuug gugacuaggg
2019920RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 199gucguucuca augacuaggg
2020020RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 200acugucucca augacuaggg
2020120RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 201gcgccaccgg uugaugugau
2020220RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 202gcgccaccgg uugauguaac
2020320RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 203gcgccaccgg uugacgugac
2020420RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 204gcgccaccgg cugaugugac
2020520RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 205gcgccgccgg uugaugugac
2020620RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 206acgccaccgg uugaugugac
2020720RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 207gcgccaccgg uugauauaau
2020820RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 208gcgccaccgg uuaauguaau
2020920RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 209gcgccaccag uugauguaau
2021020RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 210gcgucaccgg uugauguaau
2021120RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 211gcgccaccgg uugguaugau
2021220RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 212gcgccaccgg cugauaugau
2021320RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 213gcgccaucgg uugauaugau
2021420RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 214gugccaccgg uugauaugau
2021520RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 215gcgccaccgg uugauauaac
2021620RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 216gcgccaccgg uuggugcgac
2021720RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 217gcgccaccgg ucgauaugac
2021820RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 218gcgccaccga uugacgugac
2021920RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 219gcgccaccgg uugguauaau
2022020RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 220gcgccaccgg ucgacguaau
2022120RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 221gcgccaccga uugguguaau
2022220RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 222gcgccacugg uuaauguaau
2022320RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 223gcgccaccgg uugguauaac
2022420RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 224gcgccaccgg cuggugcgac
2022520RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 225gcgccacugg ucgauaugac
2022620RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 226gcgcuaccga uugacgugac
2022720RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 227gugccacugg uuggugugac
2022820RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 228gcgccaccgg uugauguggc
2022920RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 229gcgccaccgg uugaugcaau
2023020RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 230gcgccaccgg uugacaugau
2023120RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 231gcgccaccgg uuagugugau
2023220RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 232gcgccaccgg ccgaugugau
2023320RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 233gcgccaccaa uugaugugau
2023420RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 234gcgccauugg uugaugugau
2023520RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 235gcgcugccgg uugaugugau
2023620RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 236gcaucaccgg uugaugugau
2023720RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 237augccaccgg uugaugugau
2023820RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 238gcgccaccgg uugaugcagu
2023920RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 239gcgccaccgg uuggcaugau
2024020RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 240gcgccaccgg ccaaugugau
2024120RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 241gcgccacuaa uugaugugau
2024220RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 242gcgcugucgg uugaugugau
2024320RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 243guaucaccgg uugaugugau
2024420RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 244gcgccaccgg uugauacagc
2024520RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 245gcgccaccgg uuagcacgau
2024620RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 246gcgccaccga ccagugugau
2024720RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 247gcgccauuaa cugaugugau
2024820RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic
oligonucleotide" 248gcguuguugg uugaugugau 2024920RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 249auauuaccgg uugaugugau 2025020RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 250gucaccucca augacuaggg 2025120RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 251gucaccugga augacuaggg 2025220RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 252gucacgacca augacuaggg 2025320RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 253gucugcucca augacuaggg 2025420RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 254gagaccucca augacuaggg 2025520RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 255gucaccagga augacuaggg 2025620RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 256gucuggucca augacuaggg 2025720RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 257gaguccucca augacuaggg 2025820RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 258gucacgagga augacuaggg 2025920RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 259gucuggacca augacuaggg 2026020RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 260gagugcucca augacuaggg 2026120RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 261gucugcugga augacuaggg 2026220RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 262gagacgacca augacuaggg 2026320RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 263gagaccugga augacuaggg 2026440DNAHomo sapiens
264ggacatcgat gtcacctcca atgactaggg tgggcaacca 4026520RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 265gucaccucca augacuaggg 2026620RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"modified_base(20)..(20)a, c, u, g, unknown or other
266gucaccucca augacuaggn 2026720RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"modified_base(19)..(19)a, c, u, g, unknown or other
267gucaccucca augacuagng 2026820RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"modified_base(18)..(18)a, c, u, g, unknown or other
268gucaccucca augacuangg 2026920RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"modified_base(17)..(17)a, c, u, g, unknown or other
269gucaccucca augacunggg 2027020RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"modified_base(16)..(16)a, c, u, g, unknown or other
270gucaccucca augacnaggg 2027120RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"modified_base(15)..(15)a, c, u, g, unknown or other
271gucaccucca auganuaggg 2027220RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"modified_base(14)..(14)a, c, u, g, unknown or other
272gucaccucca augncuaggg 2027320RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"modified_base(13)..(13)a, c, u, g, unknown or other
273gucaccucca aunacuaggg 2027420RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"modified_base(12)..(12)a, c, u, g, unknown or other
274gucaccucca angacuaggg 2027520RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"modified_base(11)..(11)a, c, u, g, unknown or other
275gucaccucca nugacuaggg 2027620RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"modified_base(10)..(10)a, c, u, g, unknown or other
276gucaccuccn augacuaggg 2027720RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"modified_base(9)..(9)a, c, u, g, unknown or other
277gucaccucna augacuaggg 2027820RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"modified_base(8)..(8)a, c, u, g, unknown or other
278gucaccunca augacuaggg 2027920RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"modified_base(7)..(7)a, c, u, g, unknown or other
279gucaccncca augacuaggg 2028020RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"modified_base(6)..(6)a, c, u, g, unknown or other
280gucacnucca augacuaggg 2028120RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"modified_base(5)..(5)a, c, u, g, unknown or other
281gucancucca augacuaggg 2028220RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"modified_base(4)..(4)a, c, u, g, unknown or other
282gucnccucca augacuaggg 2028320RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"modified_base(3)..(3)a, c, u, g, unknown or other
283gunaccucca augacuaggg 2028420RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"modified_base(2)..(2)a, c, u, g, unknown or other
284gncaccucca augacuaggg 2028520RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 285gucaccucca augacuaggg 2028620RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 286gacaucgaug uccuccccau 2028720RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 287gcgccaccgg uugaugugau 2028820RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 288gucaccucca augacuaggg 2028920RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 289gucaccucca augacuagaa 2029020RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 290gucaccucca augacugagg 2029120RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 291gucaccucca augaucaggg 2029220RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 292gucaccucca auagcuaggg 2029320RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 293gucaccucca gcgacuaggg 2029420RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 294gucaccucug augacuaggg 2029520RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 295gucacccuca augacuaggg 2029620RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 296gucauuucca augacuaggg 2029720RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 297guugccucca augacuaggg 2029820RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 298accaccucca augacuaggg 2029920RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 299gucaccucca augacugaag 2030020RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 300gucaccucca auggucaggg 2030120RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 301gucaccucca gcaacuaggg 2030220RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 302gucaccuuug augacuaggg 2030320RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 303gucauuccca augacuaggg 2030420RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 304gcugccucca augacuaggg 2030520RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 305gucaccucca augaccgaaa 2030620RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 306gucaccucca auagucgggg 2030720RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 307gucaccuccg gcagcuaggg 2030820RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 308gucacccuug gugacuaggg 2030920RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 309gucguucuca augacuaggg 2031020RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 310acugucucca augacuaggg 2031120RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 311gcgccaccgg uugaugugau 2031220RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 312gcgccaccgg uugauguggc 2031320RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 313gcgccaccgg uugaugcaau 2031420RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 314gcgccaccgg uugacaugau 2031520RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 315gcgccaccgg uuagugugau 2031620RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 316gcgccaccgg ccgaugugau 2031720RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 317gcgccaccaa uugaugugau 2031820RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 318gcgccauugg uugaugugau 2031920RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 319gcgcugccgg uugaugugau 2032020RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 320gcaucaccgg uugaugugau 2032120RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 321augccaccgg uugaugugau 2032220RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 322gcgccaccgg uugaugcagu 2032320RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 323gcgccaccgg uuggcaugau 2032420RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 324gcgccaccgg ccaaugugau 2032520RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 325gcgccacuaa uugaugugau 2032620RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 326gcgcugucgg uugaugugau 2032720RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 327guaucaccgg uugaugugau 2032820RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 328gcgccaccgg uugauacagc 2032920RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 329gcgccaccgg uuagcacgau 2033020RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 330gcgccaccga ggagugugau 2033120RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 331gcgccauuaa gugaugugau 2033220RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 332gcguuguugg uugaugugau 2033320RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 333auauuaccgg uugaugugau 2033420RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 334gucaccucca augacuaggg 2033520RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 335gucaccucca augacuaaga 2033620RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 336gucaccucca augauuagga 2033720RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 337gucaccucca gugacuagga 2033820RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 338gucacuucca augacuagga 2033920RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 339aucaccucca augacuagga 2034020RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 340gucaccucca augacuaggg 2034120RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 341gucaccucca augaccaaga 2034220RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 342gucaccucca auggcuggga 2034320RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 343gucaccucca acgaccagga 2034420RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 344gucaccuccg augauuagga 2034520RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 345gucaccucca auggccaagg 2034620RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 346gucaccucca acgauuaagg 2034720RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 347gucaccuccg auggcuaagg 2034820RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 348gucaccuuca auaacuaagg 2034920RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 349gucaccucca auggccaaga 2035020RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 350gucaccucca guggcuggga 2035120RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 351gucaccuuca acgaccagga 2035220RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 352gucaucuccg augauuagga 2035320RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 353gccaccuuca auggcuagga 2035420RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 354gcgccaccgg uugaugugau 2035520RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 355gcgccaccgg uugauguaac 2035620RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 356gcgccaccgg uugacgugac 2035720RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 357gcgccaccgg cugaugugac 2035820RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 358gcgccgccgg uugaugugac 2035920RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 359acgccaccgg uugaugugac 2036020RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 360gcgccaccgg uugaugugau 2036120RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 361gcgccaccgg uugauauaac 2036220RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 362gcgccaccgg uuggugcgac 2036320RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 363gcgccaccgg ucgauaugac 2036420RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 364gcgccaccga uugacgugac 2036520RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 365gcgccaccgg uugguauaau 2036620RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 366gcgccaccgg ucgacguaau 2036720RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 367gcgccaccga uugguguaau 2036820RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 368gcgccacugg uuaauguaau 2036920RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 369gcgccaccgg uugguauaac 2037020RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 370gcgccaccgg cuggugcgac 2037120RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 371gcgccacugg ucgauaugac 2037220RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 372gcgcuaccga uugacgugac 2037320RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 373gugccacugg uuggugugac 2037423RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 374gaguccgagc agaagaagaa ggg 2337523DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 375gauuccuacc agaagaagaa tgg
2337623RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 376aaguccgagg agaggaagaa agg
2337723RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 377aaguccgaga agaagcagaa aag
2337823RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 378gaguuccaga agaagaagaa gag
2337923RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 379gagucugaac ggaagaagaa aag
2338023RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 380gaguuugagu agaagaagaa gag
2338123RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 381gagucccaga agaagaaaaa aag
2338223DNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide"source/note="Description of
Combined DNA/RNA Molecule Synthetic oligonucleotide" 382gacuccgagc
agcagaagga tgg 2338323RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 383gagucagagc agaacuagaa ggg 2338423RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 384gaggccgagc agaagaaaga cgg 2338523RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 385gagucagacc aggagaagaa gag 2338623DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 386gaguccaaga agaauaagaa tag
2338723RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 387gaguaagaga agaagaagaa ggg
2338823RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 388gaguaggagg agaagaagaa agg
2338923DNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide"source/note="Description of
Combined DNA/RNA Molecule Synthetic oligonucleotide" 389aagucugagc
acaagaagaa tgg 2339023RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 390gagcccgagc agaaggagga ggg 2339123RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 391gaguuagagc agaagaagaa agg 2339223RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 392gagucagaac agaagaacaa cag 2339323DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 393acgucugagc agaagaagaa tgg
2339423RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 394gaguacuaga agaagaagaa aag
2339523RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 395aagucugagc agaagaagca cag
2339623RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 396gagucccagc agaggaagca gag
2339723RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 397gagucugggc aggagaagaa gag
2339823RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 398gaguccuagc aggagaagaa gag
2339923RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 399gaggaggagc agaagaagaa aag
2340023RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 400gaguccgaga aaaugaagaa gag
2340123RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 401gaggcccagc agaggaagaa gag
2340223RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 402gaauccaagc agaagaagag aag
2340323DNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide"source/note="Description of
Combined DNA/RNA Molecule Synthetic oligonucleotide" 403gacuccuagc
aaaagaagaa tgg 2340423RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 404gagucccagc aaaagaagaa aag 2340523RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 405gagucuaagc agaagaagaa gag 2340623RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 406aagucagagg agaagaagaa ggg 2340723DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 407gaguccaagc agaagaagga tag
2340823RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 408gagacugaga agaagaagaa agg
2340923RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 409gagucccagg agaagaagag agg
2341023RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 410gagucccagg agaagaaaaa cag
2341123RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 411gagagcaagc agaagaagaa aag
2341223RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 412gaguccaagc auaagaaaaa cag
2341323RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 413gaguccaagc aguagaggaa ggg
2341423RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 414gaguccggga aggagaagaa agg
2341520RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 415gcgccaccgg uugaugugau
2041623RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 416gggccauggg uugaugugau gag
2341723DNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide"source/note="Description of
Combined DNA/RNA Molecule Synthetic oligonucleotide" 417gtcacctcca
atgactaggg tgg 2341823DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 418gcuacctcca gtgactaggg agg
2341923DNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide"source/note="Description of
Combined DNA/RNA Molecule Synthetic oligonucleotide" 419gtgacctcca
atgcctagag ggg 2342023DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 420gtcacctcca ctucctaggg cag
2342123DNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide"source/note="Description of
Combined DNA/RNA Molecule Synthetic oligonucleotide" 421utcacctcca
aaaactaggg aag 2342223DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 422gtcaactcca atggctuggg agg
2342323DNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide"source/note="Description of
Combined DNA/RNA Molecule Synthetic oligonucleotide" 423gtcacctuaa
atgactuggg aag 2342423DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 424gtcacutcca aggactagag aag
2342523DNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide"source/note="Description of
Combined DNA/RNA Molecule Synthetic oligonucleotide" 425gtcacctcca
ggguctaggg cag 2342623DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 426gtcacctcca ctguataggg agg
2342723DNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide"source/note="Description of
Combined DNA/RNA Molecule Synthetic oligonucleotide" 427gtcaactcca
atgautagga cag 2342823DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 428gtggtgtcac gctcgtcgtt tgg 2342923DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 429tccagtctat taattgttgc cgg 2343012DNAHomo
sapiens 430tagcgggtaa gc 1243112DNAHomo sapiens 431tcggtcacat gt
1243212DNAHomo sapiens 432actccccgta gg 1243312DNAHomo sapiens
433actgcgtgtt aa 1243412DNAHomo sapiens 434acgtcgcctg at
1243512DNAHomo sapiens 435taggtcgacc ag 1243612DNAHomo sapiens
436ggcgttaatg at 1243712DNAHomo sapiens 437tgtcgcatgt ta
1243812DNAHomo sapiens 438atggaaacgc at 1243912DNAHomo sapiens
439gccgaattcc tc 1244012DNAHomo sapiens 440gcatggtacg ga
1244112DNAHomo sapiens 441cggtactctt ac 1244212DNAHomo sapiens
442gcctgtgccg ta 1244312DNAHomo sapiens 443tacggtaagt cg
1244412DNAHomo sapiens 444cacgaaatta cc 1244512DNAHomo sapiens
445aaccaagata cg 1244612DNAHomo sapiens 446gagtcgatac gc
1244712DNAHomo sapiens 447gtctcacgat cg 1244812DNAHomo sapiens
448tcgtcgggtg ca 1244912DNAHomo sapiens 449actccgtagt ga
1245012DNAHomo sapiens 450caggacgtcc gt 1245112DNAHomo sapiens
451tcgtatccct ac 1245212DNAHomo sapiens 452tttcaaggcc gg
1245312DNAHomo sapiens 453cgccggtgga at 1245412DNAHomo sapiens
454gaacccgtcc ta 1245512DNAHomo sapiens 455gattcatcag cg
1245612DNAHomo sapiens 456acaccggtct tc 1245712DNAHomo sapiens
457atcgtgccct aa 1245812DNAHomo sapiens 458gcgtcaatgt tc
1245912DNAHomo sapiens 459ctccgtatct cg 1246012DNAHomo sapiens
460ccgattcctt cg 1246112DNAHomo sapiens 461tgcgcctcca gt
1246212DNAHomo sapiens 462taacgtcgga gc 1246312DNAHomo sapiens
463aaggtcgccc at 1246412DNAHomo sapiens 464ctcggggact at
1246512DNAHomo sapiens 465ttcgagcgat tt 1246612DNAHomo sapiens
466tgagtcgtcg ag 1246712DNAHomo sapiens 467tttacgcaga gg
1246812DNAHomo sapiens 468aggaagtatc gc 1246912DNAHomo sapiens
469actcgatacc at 1247012DNAHomo sapiens 470cgctacatag ca
1247112DNAHomo sapiens 471ttcataaccg gc 1247212DNAHomo sapiens
472ccaaacggtt aa 1247312DNAHomo sapiens 473cgattccttc gt
1247412DNAHomo sapiens 474cgtcatgaat aa 1247512DNAHomo sapiens
475agtggcgatg ac 1247612DNAHomo sapiens 476cccctacggc ac
1247712DNAHomo sapiens 477gccaacccgc ac 1247812DNAHomo sapiens
478tgggacaccg gt 1247912DNAHomo sapiens 479ttgactgcgg cg
1248012DNAHomo sapiens 480actatgcgta gg 1248112DNAHomo sapiens
481tcacccaaag cg 1248212DNAHomo sapiens 482gcaggacgtc cg
1248312DNAHomo sapiens 483acaccgaaaa cg 1248412DNAHomo sapiens
484cggtgtattg ag 1248512DNAHomo sapiens 485cacgaggtat gc
1248612DNAHomo sapiens 486taaagcgacc cg 1248712DNAHomo sapiens
487cttagtcggc ca 1248812DNAHomo sapiens 488cgaaaacgtg gc
1248912DNAHomo sapiens 489cgtgccctga ac 1249012DNAHomo sapiens
490tttaccatcg aa 1249112DNAHomo sapiens 491cgtagccatg tt
1249212DNAHomo sapiens 492cccaaacggt ta 1249312DNAHomo sapiens
493gcgttatcag aa 1249412DNAHomo sapiens 494tcgatggtaa ac
1249512DNAHomo sapiens 495cgactttttg ca 1249612DNAHomo sapiens
496tcgacgactc ac 1249712DNAHomo sapiens 497acgcgtcaga ta
1249812DNAHomo sapiens 498cgtacggcac ag 1249912DNAHomo sapiens
499ctatgccgtg ca 1250012DNAHomo sapiens 500cgcgtcagat at
1250112DNAHomo sapiens 501aagatcggta gc 1250212DNAHomo sapiens
502cttcgcaagg ag 1250312DNAHomo sapiens 503gtcgtggact ac
1250412DNAHomo sapiens 504ggtcgtcatc aa 1250512DNAHomo sapiens
505gttaacagcg tg 1250612DNAHomo sapiens 506tagctaaccg tt
1250712DNAHomo sapiens 507agtaaaggcg ct 1250812DNAHomo sapiens
508ggtaatttcg tg 1250920DNAMus musculus 509gactgattca gctagaccgg
2051020DNAMus musculus 510ggcacaaacg cgttcaatac 2051120DNAMus
musculus 511aacgcgttca ataccggtct 2051220DNARattus norvegicus
512gctaggagga tctcgggcgg 2051320DNARattus norvegicus 513gttccagccc
gcgactaagc 2051420DNARattus norvegicus 514gtgagggggt tgcaacgagg
2051520DNARattus norvegicus 515accgcaactc ttggcgcgtc
2051620DNARattus norvegicus 516accagatgcg gttgcagtcg
2051720DNARattus norvegicus 517tacttcggcg cgtattttta
2051820DNARattus norvegicus 518aaatgtgaga ttccggacta
2051920DNARattus norvegicus 519tgtgaccgcg cttgcgaact
2052020DNARattus norvegicus 520tctcgggcac cgcgagccgg
2052120DNARattus norvegicus 521cgctcctggc tggtccgcaa
2052220DNARattus norvegicus 522tgagggtagt tgagcgccgt
2052320DNARattus norvegicus 523tgagattggg accgccacgc
2052420DNARattus norvegicus 524ttgatataga ctttacgtgc
2052520DNARattus norvegicus 525agctaactac ccccccgggt
2052620DNARattus norvegicus 526cacctccagc atcgggcgag
2052720DNARattus norvegicus 527aggcggcgtc gtcgggatgt
2052820DNARattus norvegicus 528ccgtttggcc cccagttcgg
2052920DNARattus norvegicus 529ttccagcccg cgactaagcg
2053020DNARattus norvegicus 530agagtgaggg ggttgcaacg
2053120DNARattus norvegicus 531ccgcaactct tggcgcgtcg
2053220DNARattus norvegicus 532tgatcccgct cgcccgatgc
2053320DNARattus norvegicus 533ctggaagagg cggcgtcgtc
2053420DNARattus norvegicus 534aaccccgctt agtcgcgggc
2053520DNARattus norvegicus 535attgggaccg ccacgccggt
2053620DNARattus norvegicus 536tccagaaccg cgtctcgacg
2053720DNARattus norvegicus 537cgcgcacaga acgcgtacgc
2053820DNARattus norvegicus 538gtgaaacccc gcttagtcgc
2053920DNARattus norvegicus 539ccccgacgcg ccaagagttg
2054020DNARattus norvegicus 540accgcgtctc gacgcggaga
2054120DNARattus norvegicus 541ccgcgcacag aacgcgtacg
2054220DNARattus norvegicus 542ggtgaaaccc cgcttagtcg
2054320DNARattus norvegicus 543actcttggcg cgtcggggca
2054420DNARattus norvegicus 544tccttctccg cgtcgagacg
2054520DNARattus norvegicus 545acggtccgga cctccgaact
2054620DNARattus norvegicus 546ggcgccagcc gctcgctcta
2054720DNARattus norvegicus 547gacttccgag ttcgcaagcg
2054820DNARattus norvegicus 548tgcgaactcg gaagtccaat
2054920DNARattus norvegicus 549tctggaagag gcggcgtcgt
2055020DNARattus norvegicus 550tcttagcccc gagacccggt
2055120DNARattus norvegicus 551ccctccggcg ccgtgttttc
2055220DNARattus norvegicus 552ccagaaaaca cggcgccgga
2055317DNADanio rerio 553atcaatatct cgcccag 1755417DNADanio rerio
554attaaacgtg ttccact 1755517DNADanio rerio 555acgtttaatc gctttct
1755617DNADanio rerio 556caagatgcgt acggtca 1755717DNADanio rerio
557tctgtgtact cgaatat 1755817DNADanio rerio 558tcctgacttc tgggcac
1755917DNADanio rerio 559cccggtgccc agaagtc 1756017DNADanio rerio
560ccggagatcc attcatg 1756117DNADanio rerio 561tgatgaccca gaatcaa
1756217DNADanio rerio 562tgaatgaccg gtcctcc 1756317DNADanio rerio
563tccaaagggg cttcgga 1756417DNADanio rerio 564cttcagttcg ctgtgca
1756517DNADanio rerio 565gatacacgcc ggcataa 1756617DNADanio rerio
566cacacgagga tacacgc 1756717DNADanio rerio 567tcgctgtcct cttcact
1756817DNADanio rerio 568ttaaagtgat tcccagt 1756917DNADanio rerio
569cagctgatcc tccaact 1757017DNADanio rerio 570atatttagag taagaat
1757117DNADrosophila melanogaster 571aatttttatc attcagt
1757220DNADrosophila melanogaster 572gattcttcca tgatcggcca
2057320DNADrosophila melanogaster 573cgattgaaaa tccacataaa
2057420DNADrosophila melanogaster 574gctgatttcg gacaaaagtc
2057520DNADrosophila melanogaster 575aaacggatga cgtgcaacgt
2057620DNADrosophila melanogaster 576cgttttgtgt gcgtgcgcac
2057720DNADrosophila melanogaster 577ggaataatct tcgtctatcc
2057820DNADrosophila melanogaster 578ttcgctcacg aacaccaaca
2057920DNADrosophila melanogaster 579acaaagtaca tagggtgttt
2058020DNADrosophila melanogaster 580ccatcccccg taaagagggt
2058120DNADrosophila melanogaster 581tatttttagt tggtaaatta
2058217DNADrosophila melanogaster 582attcactctg ggttatg
1758320DNADrosophila melanogaster 583ctgctttcct tggccgatca
2058420DNADrosophila melanogaster 584gattatttgc tccatttatg
2058520DNADrosophila melanogaster 585ctctgtacag aatattttct
2058620DNADrosophila melanogaster 586gcagcggcag tgagagacgt
2058720DNADrosophila melanogaster 587tgtgcgcacg cacacaaaac
2058820DNADrosophila melanogaster 588aattcgctaa ttgggcagcc
2058920DNADrosophila melanogaster 589caaggaaatt gacagatgta
2059020DNADrosophila melanogaster 590gctctagcaa ccacgtcaga
2059120DNADrosophila melanogaster 591acaatacgaa tgcgagaagg
2059220DNADrosophila melanogaster 592aaataaaaat tagtgtccgc
2059317DNADrosophila melanogaster 593agagtgaata aattaca
1759420DNADrosophila melanogaster 594ttattcaaga cttacaccat
2059520DNADrosophila melanogaster 595attatgacgt tttttggtac
2059620DNADrosophila melanogaster 596ttcgggcggt ctaaagcagt
2059720DNADrosophila melanogaster 597tgttactttt tcgaacgttg
2059820DNADrosophila melanogaster 598gttttgtgtg cgtgcgcaca
2059920DNADrosophila melanogaster 599tgcccaatta gcgaattagg
2060020DNADrosophila melanogaster 600cgccccaaaa gaaaatacgt
2060120DNADrosophila melanogaster 601gctgcggctt tcgtgaaatg
2060220DNADrosophila melanogaster 602aaaaggagga gaacatatct
2060317DNADrosophila melanogaster 603atgtaattta ttcactc
1760420DNADrosophila melanogaster 604agaactaagc acgtattttt
2060520DNADrosophila melanogaster 605acaaacaatc caaataaaag
2060620DNADrosophila melanogaster 606caacaaaaaa gtgagctctg
2060720DNADrosophila melanogaster 607cgttgttcgt ttttgtagtt
2060820DNADrosophila melanogaster 608ctctttctgg taaatgctgt
2060920DNADrosophila melanogaster 609ttaggtggga ttttcgtgta
2061020DNADrosophila melanogaster 610ctacaaaact acacgaatta
2061120DNADrosophila melanogaster 611gctctcgctc tgcctcgctg
2061220DNADrosophila melanogaster 612aaaggaggag aacatatctt
2061317DNADrosophila melanogaster 613aatcaaatcg gtacatt
1761420DNADrosophila melanogaster 614atttcagctc tcaacgcaaa
2061520DNADrosophila melanogaster 615attctgaaac cacttttatt
2061620DNADrosophila melanogaster 616gagctctgcg gtagtgacgc
2061720DNADrosophila melanogaster 617ctacaaaaac gaacaacgca
2061820DNADrosophila melanogaster 618tactaaacac atactaaaac
2061920DNADrosophila melanogaster 619gtcgatttgt agaactggtg
2062020DNADrosophila melanogaster 620tggtactttt ctctcgtaca
2062120DNADrosophila melanogaster 621ttccatgctt ctttagtgca
2062220DNADrosophila melanogaster 622attaaaagcg tatttttagt
2062317DNADrosophila melanogaster 623ggttcgacac gcagaaa
1762420DNADrosophila melanogaster 624tggagcaaat aatccattca
2062520DNADrosophila melanogaster 625gctctgtaca gaatattttc
2062620DNADrosophila melanogaster 626atgttacttt ttcgaacgtt
2062720DNADrosophila melanogaster 627atcccgttag tgtgtgagat
2062820DNADrosophila melanogaster 628taggtgggat tttcgtgtag
2062920DNADrosophila melanogaster 629tctttatgta cacgagtata
2063020DNADrosophila melanogaster 630ttccatgcac taaagaagca
2063120DNADrosophila melanogaster 631ggtcgcgtcg ataccatttt
2063220DNADrosophila melanogaster 632ctttcttcct cttcttcggg
2063320DNADrosophila melanogaster 633catgttactt tttcgaacgt
2063420DNADrosophila melanogaster 634tccccaatct cacacactaa
2063520DNADrosophila melanogaster 635tgtaggggtc cctatagtag
2063620DNADrosophila melanogaster 636cacaaagtac atagggtgtt
2063720DNADrosophila melanogaster 637tcccccatcc cccgtaaaga
2063820DNADrosophila melanogaster 638ctttagaccg cccgaagaag
2063920DNADrosophila melanogaster 639tacaaaaacg aacaacgcaa
2064020DNADrosophila melanogaster 640gttgaatcgc ctgtctttaa
2064120DNADrosophila melanogaster 641ccgcgctgct catccaaact
2064220DNADrosophila melanogaster 642cttcagcgca caaagtacat
2064320DNADrosophila melanogaster 643gcaacaatac gaatgcgaga
2064420DNADrosophila melanogaster 644gccggcagag gcagtgaccg
2064520DNADrosophila melanogaster 645cgcaagggag caatgggtac
2064620DNADrosophila melanogaster 646ttgaatcgcc tgtctttaat
2064720DNADrosophila melanogaster 647agaaactttc acggcaccgt
2064820DNADrosophila melanogaster 648gcaagcgaaa tggcgcttcg
2064920DNADrosophila melanogaster 649agaggcagtg accgaggcag
2065020DNADrosophila melanogaster 650ggaaaaaacc cattaaagac
2065120DNADrosophila melanogaster 651atgcacggaa taagctccaa
2065220DNADrosophila melanogaster 652tgtaccacca accctcttta
2065320DNADrosophila melanogaster 653gaaatcttca ttccctccaa
2065420DNADrosophila melanogaster 654aaggaaattg acagatgtat
2065520DNADrosophila melanogaster 655gtaccaccaa ccctctttac
2065620DNADrosophila melanogaster 656cgcaaaaagc ctctactata
2065720DNADrosophila melanogaster 657taccaccaac cctctttacg
2065820DNADrosophila melanogaster 658tcgcaaaaag cctctactat
2065920DNADrosophila melanogaster 659accaccaacc ctctttacgg
2066020DNADrosophila melanogaster 660ccaaccctct ttacggggga
2066120DNADrosophila melanogaster 661caaccctctt tacgggggat
2066220DNADrosophila melanogaster 662atcccccatc ccccgtaaag
2066317DNACaenorhabditis elegans 663aagagagtca gattgga
1766417DNACaenorhabditis elegans 664tctgactctc ttgtgtt
1766517DNACaenorhabditis elegans 665acacaagaga gtcagat
1766617DNACaenorhabditis elegans 666attttcaggt aaaagtt
1766717DNACaenorhabditis elegans 667gaagacgctc gaattct
1766817DNACaenorhabditis elegans 668tatgtgacgt cttatct
1766917DNACaenorhabditis elegans 669tgttcctctt gatcatc
1767017DNACaenorhabditis elegans 670aagaggaaca taatcta
1767117DNACaenorhabditis elegans 671tctaggccac gtcttgc
1767217DNACaenorhabditis elegans 672agtattctat attcagt
1767317DNACaenorhabditis elegans 673tgcatggttt gaatctt
1767417DNACaenorhabditis elegans 674gcaactgatg tgtttct
1767517DNACaenorhabditis elegans 675gatgtgtttc taggctc
1767617DNACaenorhabditis elegans 676aagttctaga tcacgag
1767717DNACaenorhabditis elegans 677tcccaaatca tccttga
1767817DNACaenorhabditis elegans 678tgggttttaa accatca
1767917DNACaenorhabditis elegans 679gacgggttgg aggcaga
1768017DNACaenorhabditis elegans 680cctccaaccc gtccaat
1768117DNACaenorhabditis elegans 681aaaccgattg gacgggt
1768217DNACaenorhabditis elegans 682ttggaaaccg attggac
1768317DNACaenorhabditis elegans 683tgtctattgt acaagct
1768417DNACaenorhabditis elegans 684gtctattgta caagctt
1768517DNACaenorhabditis elegans 685tctattgtac aagcttg
1768617DNACaenorhabditis elegans 686tagcactcga cccgaaa
1768717DNACaenorhabditis elegans 687cactcgaccc gaaacgg
1768817DNACaenorhabditis elegans 688actcgacccg aaacggt
1768916DNACaenorhabditis elegans 689acccgaaacg gtggga
1669017DNACaenorhabditis elegans 690cccgaaacgg tgggaag
1769117DNACaenorhabditis elegans 691ccgaaacggt gggaagg
1769217DNACaenorhabditis elegans 692aacggtggga agggggg
1769317DNACaenorhabditis elegans 693ccccttccca ccgtttc
1769417DNACaenorhabditis elegans 694cccccttccc accgttt
1769517DNACaenorhabditis elegans 695ggggggagga catccgc
1769617DNACaenorhabditis elegans 696taggtgactg actccgg
1769717DNACaenorhabditis elegans 697ttgtaggtga ctgactc
1769817DNACaenorhabditis elegans 698ggattagctg ctccaat
1769917DNACaenorhabditis elegans 699attagctgct ccaattg
1770017DNACaenorhabditis elegans 700acttagcaca ccccaat
1770117DNACaenorhabditis elegans 701aattgtctat gtttaat
1770217DNACaenorhabditis elegans 702taatgggtgt atcgtat
1770317DNACaenorhabditis elegans 703agcacttttt gagtggt
1770417DNACaenorhabditis elegans 704aggaagtgag agtcact
1770517DNACaenorhabditis elegans 705ggaagtgaga gtcacta
1770617DNACaenorhabditis elegans 706gaatggataa agccacg
1770717DNACaenorhabditis elegans 707ccacgtggca atcatat
1770817DNACaenorhabditis elegans 708gtttatagta ggatttt
1770920DNASus scrofa 709aggatacctc aacgactacg 2071020DNASus scrofa
710gcgctgcccg acgtgtggca 2071120DNASus scrofa 711cttcaagcaa
tagggtcccg 2071220DNASus scrofa 712atgacgcata tcaaatcgca
2071320DNASus scrofa 713tattgctcta tagtcggctt 2071420DNASus scrofa
714ataaccgact tagcgaattc 2071520DNASus scrofa 715aggctgctga
ccgtattgcc 2071620DNASus scrofa 716ggatacctca acgactacgc
2071720DNASus scrofa 717ctcagccccg tgcaggccta 2071820DNASus scrofa
718taggaaagcg gctcggattg 2071920DNASus scrofa 719ggcatagacg
ggcgttaagt 2072020DNASus scrofa 720gcggtcccta gacccaaccg
2072120DNASus scrofa 721agcccgggag tctttcgcta 2072220DNASus scrofa
722ctgctagtgg cggccgttta 2072320DNASus scrofa 723gatacctcaa
cgactacgcg 2072420DNASus scrofa 724ttttcccagc aatagggtcg
2072520DNASus scrofa 725cgaatgcccg ggtacgaggg 2072620DNASus scrofa
726ggaaagaaag tctgcgcgtc 2072720DNASus scrofa 727cgggccccgc
gtagtcgttg 2072820DNASus scrofa 728ggtctcctcg accctattgc
2072920DNASus scrofa 729tttatgaacg gattgcactc 2073020DNASus scrofa
730gcagaggccg gcgggtgtaa 2073120DNASus scrofa 731ggcgtcgcca
tcgtagacgg 2073220DNASus scrofa 732gtctctgcga cgcgatccag
2073320DNASus scrofa 733tcacccacca accgttcatt 2073420DNASus scrofa
734cgtctacgat ggcgacgccg 2073520DNASus scrofa 735agtctctgcg
acgcgatcca 2073620DNASus scrofa 736gtaggtgcct aatgaacggt
2073720DNASus scrofa 737tggctacatc aacggcgacg 2073820DNASus scrofa
738gagtctctgc gacgcgatcc 2073920DNASus scrofa 739ggggcccacg
gttgggtcta 2074020DNASus scrofa 740acctccgccg tcgccgtcgg
2074120DNASus scrofa 741ttggcagagg atgcgaggcg 2074220DNASus scrofa
742cccgggccct gagcgcgcct 2074320DNASus scrofa 743cggcacgctt
gcggcgagtg 2074420DNABos taurus 744cctgccagta tctcaaaccg
2074523DNAHomo sapiens 745cacatcgatg tcctccccat tcc 2374620DNAHomo
sapiens 746atggggagga catcgatgtc 2074720RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 747cacaucgaug uccuccccau 2074824DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 748caccgacatc gatgtcctcc ccat 2474924DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 749aaacatgggg aggacatcga tgtc 2475026DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 750gagggcctat ttcccatgat tccttc
26751125DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic polynucleotide" 751aaaaaaagca
ccgactcggt gccacttttt caagttgata acggactagc cttattttaa 60cttgctattt
ctagctctaa aacatgggga ggacatcgat gtcggtgttt cgtcctttcc 120acaag
12575223DNAHomo sapiens 752cacataggtg tcctccccat agg 2375323DNAHomo
sapiens 753gaaatcaagg tcctccccat agg 2375423DNAHomo sapiens
754aacttccatc tcctccccat ggg 2375523DNAHomo sapiens 755gccttctctg
tcctccccat ggg 2375623DNAHomo sapiens 756gagacccatt tcctccccat tag
2375723DNAHomo sapiens 757aaaaacaatg tcctccccat tag 2375823DNAHomo
sapiens 758gccctcgctg ccctccccat cag 2375923DNAHomo sapiens
759cacggctgtg tcctccccat tgg 2376023DNAHomo sapiens 760tgcttcactg
tcctccccat agg 2376123DNAHomo sapiens 761gtcaacgttg tcctcctcat tgg
2376223DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
primer" 762gagggcctat ttcccatgat tcc 2376323DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 763gagggcctat ttcccatgat tcc 2376422DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 764cttgtggaaa ggacgaaaca cc 2276545DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"modified_base(4)..(23)a, c, t, g, unknown or other
765aacnnnnnnn nnnnnnnnnn nnnggtgttt cgtcctttcc acaag
4576625DNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide"modified_base(6)..(25)a, c, t,
g, unknown or other 766caccgnnnnn nnnnnnnnnn nnnnn
2576725DNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide"modified_base(5)..(24)a, c, t,
g, unknown or other 767aaacnnnnnn nnnnnnnnnn nnnnc
2576854DNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 768aacaccgggt cttcgagaag
acctgtttta gagctagaaa tagcaagtta aaat 5476920RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 769gaguccgagc agaagaagaa 2077020RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 770gucaccucca augacuaggg 2077120RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 771gucaccucca augacuagaa 2077220RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 772gucaccucca augacugagg 2077320RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 773gucaccucca augaucaggg 2077420RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 774gucaccucca auagcuaggg 2077520RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 775gucaccucca gcgacuaggg 2077620RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 776gucaccucug augacuaggg 2077720RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 777gucacccuca augacuaggg 2077820RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 778gucauuucca augacuaggg 2077920RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 779guugccucca augacuaggg 2078020RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 780gucaccucca augacugaag 2078120RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 781gucaccucca auggucaggg 2078220RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 782gucaccucca gcaacuaggg 2078320RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 783gucaccuucg augacuaggg 2078420RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 784gucauuccca augacuaggg 2078520RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 785gcugccucca augacuaggg 2078620RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 786gucaccucca augaccgaaa 2078720RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 787gucaccucca auagucgggg 2078820RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 788gucaccuccg gcagcuaggg 2078920RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 789gucacccuug gugacuaggg 2079020RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 790gucguucuca augacuaggg 2079120RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 791gacaucgaug uccuccccau 2079220RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 792gacaucgaug uccuccccgc 2079320RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 793gacaucgaug uccuccuuau 2079420RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 794gacaucgaug uccuuuccau 2079520RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 795gacaucgaug ucucccccau 2079620RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 796gacaucgaug cucuccccau 2079720RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 797gacaucgaca uccuccccau 2079820RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 798gacaucagug uccuccccau 2079920RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 799gacacugaug uccuccccau 2080020RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 800gaugucgaug uccuccccau 2080120RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 801gacaucgaug uccuccuugu 2080220RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 802gacaucgaug ucccuuccau 2080320RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 803gacaucgaug cuuuccccau 2080420RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 804gacaucggca uccuccccau 2080520RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 805gacacuaaug uccuccccau 2080620RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 806ggugucgaug uccuccccau 2080720RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 807gacaucgaug uccucuuugc 2080820RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 808gacaucgaug ucucuuucau 2080920RNAArtificial
Sequencesource/note="Description of Artificial SequenceSynthetic
oligonucleotide" 809gacaucgaua cuucccccau 2081020RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 810gacaucagca cccuccccau 2081120RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 811gacgcuagug uccuccccau 2081220RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 812gaguccgagc agaagaagaa 2081320RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 813gaguccgagc agaagaaggg 2081420RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 814gaguccgagc agaagagaaa 2081520RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 815gaguccgagc agaaagagaa 2081620RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 816gaguccgagc aggggaagaa 2081720RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 817gaguccgagc gaaagaagaa 2081820RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 818gaguccgaau agaagaagaa 2081920RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 819gaguccaggc agaagaagaa 2082020RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 820gagucugagc agaagaagaa 2082120RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 821gaacccgagc agaagaagaa 2082220RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 822gaguccgagc agaagagaga 2082320RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 823gaguccgagc agagagagaa 2082420RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 824gaguccgagc gagagaagaa 2082520RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 825gaguccggau agaagaagaa 2082620RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 826gagucuaagc agaagaagaa 2082720RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 827ggacccgagc agaagaagaa 2082820RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 828gaguccgagc agaagggagg 2082920RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 829gaguccgagc agggagggaa 2083020RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 830gaguccgagu gagggaagaa 2083120RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 831gaguccagau ggaagaagaa 2083220RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 832gagccuaggc agaagaagaa 2083320RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 833gcgccaccgg uugaugugau 2083420RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 834gcgccaccgg uugauguggc 2083520RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 835gcgccaccgg uugaugcaau 2083620RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 836gcgccaccgg uugacaugau 2083720RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 837gcgccaccgg uuagugugau 2083820RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 838gcgccaccgg ccgaugugau 2083920RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 839gcgccaccaa uugaugugau 2084020RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 840gcgccauugg uugaugugau 2084120RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 841gcgcugccgg uugaugugau 2084220RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 842gcaucaccgg uugaugugau 2084320RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 843gcgccaccgg uugaugcagu 2084420RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 844gcgccaccgg uuggcaugau 2084520RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 845gcgccaccgg ccaaugugau 2084620RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 846gcgccacuaa uugaugugau 2084720RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 847gcgcugucgg uugaugugau 2084820RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 848guaucaccgg uugaugugau 2084920RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 849gcgccaccgg uugauacagc 2085020RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 850gcgccaccgg uuagcacgau 2085120RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 851gcgccaccga ccagugugau 2085220RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 852gcgccauuaa cugaugugau 2085320RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 853gcguuguugg uugaugugau 2085420RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 854gucaccucca augacuaggg 2085520RNAArtificial
Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 855gucaccucca augacuaaga
2085620RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 856gucaccucca augauuagga
2085720RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 857gucaccucca gugacuagga
2085820RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 858gucacuucca augacuagga
2085920RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 859aucaccucca augacuagga
2086020RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 860gucaccucca augaccaagg
2086120RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 861gucaccucca auaacuaagg
2086220RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 862gucaccucua augacuaagg
2086320RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 863gucgccucca augacuaagg
2086420RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 864gucaccucca auggccaggg
2086520RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 865gucaccucca gugaccaggg
2086620RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 866gucaccccca augaccaggg
2086720RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 867gccaccucca augaccaggg
2086820RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 868gacaucgaug uccuccccau
2086920RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 869gacaucgaug uccucccuag
2087020RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 870gacaucgaug uccuucccag
2087120RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 871gacaucgaug cccuccccag
2087220RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 872gacauugaug uccuccccag
2087320RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 873aacaucgaug uccuccccag
2087420RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 874gacaucgaug uccucucuau
2087520RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 875gacaucgaug ucuucccuau
2087620RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 876gacaucgacg uccucccuau
2087720RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 877gacgucgaug uccucccuau
2087820RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 878gacaucgaug uccccuccau
2087920RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 879gacaucgaug cccucuccau
2088020RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 880gacaucaaug uccucuccau
2088120RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 881ggcaucgaug uccucuccau
2088220RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 882gaguccgagc agaagaagaa
2088320RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 883gaguccgagc agaagaaaag
2088420RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 884gaguccgagc agaaaaagag
2088520RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 885gaguccgagc ggaagaagag
2088620RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 886gagucugagc agaagaagag
2088720RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 887aaguccgagc agaagaagag
2088820RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 888gaguccgagc agaagcaaaa
2088920RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 889gaguccgagc aggagaaaaa
2089020RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 890gaguccgaac agaagaaaaa
2089120RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 891gagcccgagc agaagaaaaa
2089220RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 892gaguccgagc agagggagaa
2089320RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 893gaguccgagc ggaaggagaa
2089420RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 894gaguccaagc agaaggagaa
2089520RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 895ggguccgagc agaaggagaa
2089620RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 896gcgccaccgg uugaugugau
2089720RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 897gcgccaccgg uugauguaac
2089820RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 898gcgccaccgg uugaugugac
2089920RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 899gcgccaccgg cugaugugac
2090020RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 900gcgccgccgg uugaugugac
2090120RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 901acgccaccgg uugaugugac
2090220RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 902gcgccaccgg uugauauaau
2090320RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 903gcgccaccgg uuaauguaau
2090420RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 904gcgccaccag uugauguaau
2090520RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 905gcgucaccgg uugauguaau
2090620RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 906gcgccaccgg uugguaugau
2090720RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 907gcgccaccgg cugauaugau
2090820RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 908gcgccaucgg uugauaugau
2090920RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 909gugccaccgg uugauaugau
2091020RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 910gucaccucca augacuaggg
2091120RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 911gucaccucca augaccaaga
2091220RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 912gucaccucca auggcuggga
2091320RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 913gucaccucca acgaccagga
2091420RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 914gucaccuccg augauuagga
2091520RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 915gucaccucca auggccaagg
2091620RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 916gucaccucca acgauuaagg
2091720RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 917gucaccuccg auggcuaagg
2091820RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 918gucaccuuca auaacuaagg
2091920RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 919gucaccucca auggccaaga
2092020RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 920gucaccucca guggcuggga
2092120RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 921gucaccuuca acgaccagga
2092220RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 922gucaucuccg augauuagga
2092320RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 923gccaccuuca auggcuagga
2092420RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 924gacaucgaug uccuccccau
2092520RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 925gacaucgaug uccucucuag
2092620RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 926gacaucgaug ucccccucag
2092720RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 927gacaucgaug uucucuccag
2092820RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 928gacaucgaua uccuucccag
2092920RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 929gacaucgaug uccccucuau
2093020RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 930gacaucgaug uucuuccuau
2093120RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 931gacaucgaua uccccccuau
2093220RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 932gacaucggug ucaucccuau
2093320RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 933gacaucgaug uccccucuag
2093420RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 934gacaucgaug ccccccucag
2093520RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 935gacaucggug uucucuccag
2093620RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 936gacaccgaua uccuucccag
2093720RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 937ggcaucggug ucccccccag
2093820RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 938gaguccgagc agaagaagaa
2093920RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 939gaguccgagc agaaggaaag
2094020RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 940gaguccgagc agaggaggag
2094120RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 941gaguccgagc aaaaggagag
2094220RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 942gaguccgagu agaaaaagag
2094320RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 943gaguccgagc agagggaaaa
2094420RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 944gaguccgagc aaaaaaaaaa
2094520RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 945gaguccgagu agaggaaaaa
2094620RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 946gaguccgggc aggagaaaaa
2094720RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 947gaguccgagc agagggaaag
2094820RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 948gaguccgagc ggaggaggag
2094920RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic
oligonucleotide" 949gaguccgggc aaaaggagag 2095020RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 950gaguucgagu agaaaaagag 2095120RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 951ggguccgggc agaggaagag 2095220RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 952gcgccaccgg uugaugugau 2095320RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 953gcgccaccgg uugauauaac 2095420RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 954gcgccaccgg uuggugcgac 2095520RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 955gcgccaccgg ucgauaugac 2095620RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 956gcgccaccga uugacgugac 2095720RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 957gcgccaccgg uugguauaau 2095820RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 958gcgccaccgg ucgacguaau 2095920RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 959gcgccaccga uugguguaau 2096020RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 960gcgccacugg uuaauguaau 2096120RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 961gcgccaccgg uugguauaac 2096220RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 962gcgccaccgg cuggugcgac 2096320RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 963gcgccacugg ucgauaugac 2096420RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 964gcgcuaccga uugacgugac 2096520RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 965gugccacugg uuggugugac 2096620RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 966gucaccucca augacuaggg 2096723DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 967gucaccucca augacuaggg tgg
2396823DNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide"source/note="Description of
Combined DNA/RNA Molecule Synthetic oligonucleotide" 968gucaacucca
augatuagga cag 2396923DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 969gucaccucca cutccuaggg cag
2397023DNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide"source/note="Description of
Combined DNA/RNA Molecule Synthetic oligonucleotide" 970gucactucca
aggacuagag aag 2397123DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 971gucaccucca gggtcuaggg cag
2397223DNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide"source/note="Description of
Combined DNA/RNA Molecule Synthetic oligonucleotide" 972gucaacucca
augacutggg agg 2397323DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 973gucaccutaa augacutggg aag
2397423DNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide"source/note="Description of
Combined DNA/RNA Molecule Synthetic oligonucleotide" 974tucaccucca
aaaacuaggg aag 2397523DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 975gctaccucca gugacuaggg aag
2397612DNAHomo sapiens 976tgactagggt gg 1297711DNAHomo sapiens
977tgactgggtg g 1197814DNAHomo sapiens 978tgactatagg gtgg
1497912DNAHomo sapiens 979tgactaggga ag 1298011DNAHomo sapiens
980tgactgggaa g 1198114DNAHomo sapiens 981tgactatagg gaag
1498214DNAHomo sapiens 982tgactggagg gaag 1498313DNAHomo sapiens
983tgactacggg aag 1398420RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 984gaguccgagc agaagaagaa 2098523RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 985gaguccgagc agaagaagaa ggg 2398623RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 986gaggccgagc agaagaaaga cgg 2398723RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 987gagucccagc agaggaagca gag 2398823DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 988gaguccaagc agaggaagga tag
2398923DNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide"source/note="Description of
Combined DNA/RNA Molecule Synthetic oligonucleotide" 989gagucagagc
agaactagaa ggg 2399023RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 990gagucccagg agaagaagag agg 2399123DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 991gaguccaagc agtagaggaa ggg
2399223DNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide"source/note="Description of
Combined DNA/RNA Molecule Synthetic oligonucleotide" 992gaguccgaga
aaatgaagaa gag 2399323RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 993gagucccaga agaagaaaaa aag 2399423RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 994gagucccagg agaagaaaaa cag 2399523RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 995gagucagaac agaagaacaa cag 2399623DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 996gaguccaaga agaataagaa tag
2399723RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 997gaauccaagc agaagaagag aag
2399823RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 998gagucagacc aggagaagaa gag
2399923RNAArtificial Sequencesource/note="Description of Artificial
Sequence Synthetic oligonucleotide" 999aaguccgaga agaagcagaa aag
23100023DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 1000gaguctgggc aggagaagaa gag
23100123DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 1001gaguctgaac ggaagaagaa aag
23100223DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 1002aaguctgagc agaagaagca cag
23100323RNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic oligonucleotide" 1003aaguccgagg
agaggaagaa agg 23100423RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 1004gaggcccagc agaggaagaa gag
23100523DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 1005gagutccaga agaagaagaa gag
23100623DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 1006gaguactaga agaagaagaa aag
23100723DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 1007gacucctagc aaaagaagaa tgg
23100823DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 1008gaguttgagt agaagaagaa gag
23100923RNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic oligonucleotide" 1009gaguaagaga
agaagaagaa ggg 23101023RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 1010gaguaggagg agaagaagaa agg
23101123DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 1011gagucctagc aggagaagaa gag
23101223DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 1012gatucctacc agaagaagaa tgg
23101323RNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic oligonucleotide" 1013gagucccagc
aaaagaagaa aag 23101423RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 1014gagagcaagc agaagaagaa ggg
23101523RNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic oligonucleotide" 1015aagucagagg
agaagaagaa aag 23101623DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 1016gaguctaagc agaagaagaa gag
23101723DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 1017acguctgagc agaagaagaa tgg
23101812DNAHomo sapiens 1018gaagaagaag gg 12101913DNAHomo sapiens
1019gaagaaggaa ggg 13102013DNAHomo sapiens 1020gaagaaagaa ggg
13102115DNAHomo sapiens 1021gaagaattag aaggg 15102214DNAHomo
sapiens 1022gaagaacaga aggg 14102311DNAHomo sapiens 1023gaagagaagg
g 11102411DNAHomo sapiens 1024gaagaaaagg g 11102512DNAHomo sapiens
1025gaagaaagac gg 12102611DNAHomo sapiens 1026gaagaagacg g
11102713DNAHomo sapiens 1027gaagaaaaga cgg 13102813DNAHomo sapiens
1028gaagaataga cgg 13102913DNAHomo sapiens 1029gaagaacaga cgg
13103014DNAHomo sapiens 1030gaagaataag acgg 14103112DNAHomo sapiens
1031ggagaagaag ag 12103213DNAHomo sapiens 1032ggagaaagaa gag
13103311DNAHomo sapiens 1033ggagagaaga g 11103411DNAHomo sapiens
1034ggagaaaaga g 11103514DNAHomo sapiens 1035ggagaataga agag
14103612DNAHomo sapiens 1036gaagaagaag ag 12103713DNAHomo sapiens
1037gaagaaagaa gag 13103811DNAHomo sapiens 1038gaagagaaga g
11103911DNAHomo sapiens 1039gaagaaaaga g 111040384DNAHomo sapiens
1040cccagtggct gctctggggg cctcctgagt ttctcatctg tgcccctccc
tccctggccc 60aggtgaaggt gtggttccag aaccggagga caaagtacaa acggcagaag
ctggaggagg 120aagggcctga gtccgagcag aagaagaagg gctcccatca
catcaaccgg tggcgcattg 180ccacgaagca ggccaatggg gaggacatcg
atgtcacctc caatgactag ggtgggcaac 240cacaaaccca cgagggggca
gagtgctgct tgctgctggc caggcccctg cgtgggccca 300agctggactc
tggccactcc ctggccaggc tttggggagg cctggagtca tggccccaca
360gggcttgaag cccggggccg ccat 384104123DNAHomo sapiens
1041gtcacctcca atgactaggg tgg 23104223DNAHomo sapiens
1042gacatcgatg tcctccccat tgg 23104323DNAHomo sapiens
1043gagtccgagc agaagaagaa ggg 23104423DNAHomo sapiens
1044gcgccaccgg ttgatgtgat ggg 23104523DNAHomo sapiens
1045ggggcacaga tgagaaactc agg 23104623DNAHomo sapiens
1046gtacaaacgg cagaagctgg agg 23104723DNAHomo sapiens
1047ggcagaagct ggaggaggaa
ggg 23104823DNAHomo sapiens 1048ggagcccttc ttcttctgct cgg
23104923DNAHomo sapiens 1049gggcaaccac aaacccacga ggg
23105023DNAHomo sapiens 1050gctcccatca catcaaccgg tgg
23105123DNAHomo sapiens 1051gtggcgcatt gccacgaagc agg
23105223DNAHomo sapiens 1052ggcagagtgc tgcttgctgc tgg
23105323DNAHomo sapiens 1053gcccctgcgt gggcccaagc tgg
23105423DNAHomo sapiens 1054gagtggccag agtccagctt ggg
23105523DNAHomo sapiens 1055ggcctcccca aagcctggcc agg
23105620RNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic oligonucleotide" 1056gacaucgaug
uccuccccau 20105723DNAArtificial Sequencesource/note="Description
of Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 1057gacaucgaug uccuccccau tgg
23105823DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 1058gacaucgaua gccuccccac tgg
23105923RNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic oligonucleotide" 1059gacaccgaug
ucaucuccau cag 23106023RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 1060gacaugaaug accuccccau cag
23106123RNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic oligonucleotide" 1061gaaaucaagg
uccuccccau agg 23106223RNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 1062cacauaggug uccuccccau agg
23106320RNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic oligonucleotide" 1063gcgccaccgg
uugaugugau 20106423DNAArtificial Sequencesource/note="Description
of Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 1064gcgccaccgg uugaugugau tgg
23106523DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic
oligonucleotide"source/note="Description of Combined DNA/RNA
Molecule Synthetic oligonucleotide" 1065gggccatggg uugaugugac gag
23106627DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic oligonucleotide" 1066gcccgggtgg
aactggtagc catgaat 27106726DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 1067gttgaagatg aagcccagag cggagt
26106827DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic oligonucleotide" 1068gcttccgacg
aggtggccat caaggat 27106927DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 1069gcaccatctc tccgtggtac cccgggt
27107026DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic oligonucleotide" 1070ggtggaactg
gtagccatga atgaga 26107127DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 1071gccatgaatg agaccgaccc aaagagc
27107227DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic oligonucleotide" 1072gcatcctcgt
gggcacttcc gacgagg 27107327DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 1073gcagagcgga gtgctgttct cccaagt
27107426DNAArtificial Sequencesource/note="Description of
Artificial Sequence Synthetic oligonucleotide" 1074ggtcggtctc
attcatggct accagt 26107526DNAArtificial
Sequencesource/note="Description of Artificial Sequence Synthetic
oligonucleotide" 1075gcaataaaag gtgctattgc tatagt 26107620DNASus
scrofa 1076gggagtcttt cgctagggtg 20
* * * * *
References